06_trophic_positions_from_stable_isotopes
Peter Flood
2024-03-04
1 Load libraries
library(tidyverse) #for data manipulation and plotting via ggplot2
library(tRophicPosition) #Bayesian models of trophic position for stable isotopes
library(openxlsx) #importing and exporting Excel files
library(ggpubr) #arranging multiple ggplots
library(ggrepel) #make text not overlap during plotting2 Load and format post-invasion data
#read in data
all.isotopes <- read.csv(file = "Data/Stable Isotopes/isotope_merge_all.csv", header = T)Look at sample sizes per species size class per habitat per season per slough
#sample sizes for each species
#what are the NA samples?
na.samples <- filter(all.isotopes, is.na(Habitat), is.na(Slough), is.na(Season))
sample.sizes <- all.isotopes %>%
filter(!is.na(d15N), !is.na(d13C), !is.na(Habitat)) %>%
mutate(size_class = case_when(is.na(size_class) ~ .$Species, T ~ size_class),
Habitat = case_when(Habitat %in% c("Mangrove", "Edge") ~ "NP", T ~ Habitat)) %>%
group_by(size_class, Slough, Habitat, Season) %>% tally() %>%
pivot_wider(names_from = c(Slough, Habitat, Season), values_from = n) %>%
rename(`Size Class` = size_class) %>%
select(`Size Class`, SRS_Marsh_Wet, SRS_NP_Wet, SRS_Pond_Wet,
SRS_Marsh_Dry, SRS_NP_Dry, SRS_Pond_Dry,
TSL_Marsh_Wet, TSL_NP_Wet, TSL_Pond_Wet,
TSL_Marsh_Dry, TSL_NP_Dry, TSL_Pond_Dry)Some data exploration and formatting
#filter out extraneous habitat types & NAs
pond.np.marsh <- filter(all.isotopes, Habitat %in% c("Pond", "NP", "Marsh"), !is.na(d15N), !is.na(d13C))
#create group column to use in modelling function
pond.np.marsh <- pond.np.marsh %>% mutate(Community = paste(Slough, "-", Habitat, "-", Season, sep = ""))
#calculate d15N range for each community
d15n.range <- pond.np.marsh %>% group_by(Community) %>%
summarise(
Max = max(d15N),
Min = min(d15N),
Range = max(d15N) - min(d15N),
Mean = mean(d15N),
Efficiency = (max(d15N) - min(d15N)) / (max(d15N) / 3.4)
) %>%
mutate(across(where(is.numeric), round, 2))
#select columns I'll need for the analysis
model.data <- pond.np.marsh %>% select(Species, d13C, d15N, Community, Sample_Type) %>%
mutate(Species = gsub("[\\**]", "", Species))3 Load and format pre-invasion data
pre.invasion.isotopes <- read.csv(file = "Data/Stable Isotopes/FCE1263_Food_Webs_2023_manuscript_stable_isotope_data.csv")
pre.invasion.n2 <- pre.invasion.isotopes %>% group_by(Species) %>% filter(n() > 2)
#Loftus 2000 dissertation did not collect floc for stable isotope analysis
#create floc baseline group based on values from Williams and Trexler 2006
#Williams and Trexler is the closest temporally and also from the same region of the Everglades
#creating dataframe that uses mean and sd for d13C and d15N for floc from that paper
wt.floc <- data.frame(meand15N = 2.411579, SDd15N = 0.9437706,
meand13C = -29.02444, SDd13C = 1.813519)
#simulate pre-invasion floc data based on wt mean and sd
pre.invasion.floc <- data.frame(d15N = c(rnorm(mean = wt.floc$meand15N, sd = wt.floc$SDd15N, n = 20)),
d13C = c(rnorm(mean = wt.floc$meand13C, sd = wt.floc$SDd13C, n = 20)),
Species = "FLOC",
Sample_Type = "b1")
#create pre-invasion green algae group
pre.invasion.green.algae <- data.frame(d15N = c(rnorm(mean = 3.9, sd = 0.422, n = 20)),
d13C = c(rnorm(mean = -31.9, sd = 1.12, n = 20)),
Species = "Green Algae",
Sample_Type = "b2")
#Green Algae
#Simulating data based on values from Loftus's dissertation/ my first chapter
green.algae.C<-rnorm(n = 240, mean = -31.9, sd = 1.12) %>% data.frame()
green.algae.N<-rnorm(n = 240, mean = 3.9, sd = 0.422) %>% data.frame()
green.algae<-bind_cols(green.algae.C, green.algae.N)
colnames(green.algae)<-c("d13C", "d15N")
green.algae$Species<-"Green Algae"
green.algae$Community <- rep(c("TSL-Marsh-Wet", "TSL-NP-Wet", "TSL-Pond-Wet",
"TSL-Marsh-Dry", "TSL-NP-Dry", "TSL-Pond-Dry",
"SRS-Marsh-Wet", "SRS-NP-Wet", "SRS-Pond-Wet",
"SRS-Marsh-Dry", "SRS-NP-Dry", "SRS-Pond-Dry"),
each = 20)
green.algae$Sample_Type <- "b2"
#check floc sample size among habitat-season levels for both sloughs
floc.n <- model.data %>% filter(Species == "FLOC") %>%
group_by(Community) %>% tally()
#There is no TSL-NP-Dry floc
#will need to simulate some data here too
#let's see how variable floc values are first
floc <- filter(all.isotopes, Species == "FLOC")
#summarize by communities
floc.iso.summary <- model.data %>% filter(Species == "FLOC") %>% group_by(Community) %>%
summarise(meand15N = mean(d15N), meand13C = mean(d13C),
sdd15N = sd(d15N), sdd13C = sd(d13C))
#mean is extrapolate from the difference between ponds and NP in the wet season
#sd is the mean sd for floc across all habitat season levels
#mean d15N
floc.iso.summary[10,2] - (floc.iso.summary[11,2]-floc.iso.summary[9,2])
#sd d15N
mean(floc.iso.summary$sdd15N)
#mean d13C
floc.iso.summary[10,3] - (floc.iso.summary[11,3]-floc.iso.summary[9,3])
#sd d13C
mean(floc.iso.summary$sdd13C)
#simulate data
floc.n.tsl.np.dry <- rnorm(n = 15, mean = 3.36, sd = 0.631) %>% data.frame()
floc.c.tsl.np.dry <- rnorm(n = 15, mean = -25.4, sd = 1.59) %>% data.frame()
floc.tsl.np.dry <- bind_cols(floc.n.tsl.np.dry, floc.c.tsl.np.dry)
colnames(floc.tsl.np.dry) <- c("d15N", "d13C")
floc.tsl.np.dry <- floc.tsl.np.dry %>% mutate(Species = "FLOC", Sample_Type = "b1",
Community = "TSL-NP-Dry")
#combine with the rest of the data and filter for Species where n > 2
model.data.full <- bind_rows(floc.tsl.np.dry, green.algae, model.data) %>% group_by(Species, Community) %>%
filter(n() > 2) %>% as.data.frame()
#change sample_type for floc to b1
model.data.full$Sample_Type[model.data.full$Species=="FLOC"] <- "b1"
#filter for post-invaison data that matches habitat-season-slough with pre-invasion data
#pre-invasion isotopes are all from SRS wet-season marsh
post.srs.marsh.wet <- model.data.full %>% filter(Community == "SRS-Marsh-Wet")
#combine together baseline values with the rest of the isotope data
pre.invasion.baselines <- pre.invasion.n2 %>%
bind_rows(pre.invasion.floc, pre.invasion.green.algae) %>%
#change Species to match codes in model.data.full object
mutate(Species = case_when(
Species == "AMEIURUS NATALIS" ~ "AMENAT",
Species == "ANISOPTERA NAIAD" ~ "ANISOP",
Species == "BACOPA" ~ "BACCAR",
Species == "BELONESOX" ~ "BELBEL",
Species == "BELOSTOMA" ~ "BELSPP",
Species == "CICHLASOMA UROPHTHALMUS" ~ "CICURO",
Species == "CLADOCERA" ~ "CLADOC",
Species == "ELEOCHARIS CELLULOSA" ~ "ELECEL",
Species == "ERIMYZON" ~ "ERISUC",
Species == "FUNDULUS CHRYSOTUS" ~ "FUNCHR",
Species == "GAMBUSIA HOLBROOKI " ~ "GAMHOL",
Species == "HETERANDRIA" ~ "HETFOR",
Species == "LEPISOSTEUS" ~ "LEPPLA",
Species == "LEPOMIS GULOSUS" ~ "LEPGUL",
Species == "LEPOMIS MACROCHIRUS" ~ "LEPMAC",
Species == "LEPOMIS MICROLOPHUS" ~ "LEPMIC",
Species == "LEPOMIS PUNCTATUS" ~ "LEPPUN",
Species == "MICROPTERUS" ~ "MICSAL",
Species == "NYMPHEA ODORATA" ~ "NYMODO",
Species == "PALAEMONETES PALUDOSUS" ~ "PALPAL",
Species == "PANICUM HEMITOMON" ~ "PANHEM",
Species == "PASPALIDIUM GEMINATUM" ~ "PASGEM",
Species == "POECILIA" ~ "POELAT",
Species == "PONTEDARIA CORDATA" ~ "PONCOR",
Species == "PROCAMBARUS FALLAX" ~ "PROFAL",
Species == "RHYNCHOSPORA TRACYI" ~ "RHYTRA",
Species == "SAGITTARIA LANCIFOLIA" ~ "SAGLAN",
Species == "TAPHROMYSIS LOUISIANAE" ~ "TAPLOU",
Species == "TILAPIA MARIAE" ~ "TILMAR",
Species == "UTRICULARIA FOLIOSA" ~ "UTRFOL",
Species == "FLOC" ~ "FLOC",
Species == "Green Algae" ~ "Green Algae"
)) %>%
mutate(Sample_Type = case_when(
Code %in% c(7:11) ~ "vert",
Code %in% c(4:7, 13) ~ "invert",
Code == 3 ~ "om",
Species %in% c("Green Algae", "FLOC") ~ Sample_Type
)) %>% select(-c(Code)) %>%
mutate(Community = "Pre-Invasion") %>%
filter(Species %in% c(post.srs.marsh.wet$Species, "ANISOP"))
post.invasion.isotopes <- post.srs.marsh.wet %>%
mutate(Species = case_when(
Species %in% c("BRAGRA", "CELSPP", "CORING", "ERYSIM", "PACLON") ~ "ANISOP",
!Species %in% c("BRAGRA", "CELSPP", "CORING", "ERYSIM", "PACLON") ~ Species
)) %>%
filter(Species %in% c(pre.invasion.baselines$Species)) %>%
mutate(Community = "Post-Invasion")
pre.post.invasion <- bind_rows(pre.invasion.baselines, post.invasion.isotopes) %>%
as.data.frame()
#write this out to excel to use in SIBER
write.csv(pre.post.invasion,
file = "Outputs/Stable Isotopes/Pre-vs-Post-Invasion/pre_post_invasion_isotopes.csv",
row.names = F)4 Post-Invasion tRophicPosition models
everglades.list <- extractIsotopeData(model.data.full, b1 = "b1", b2 = "b2", baselineColumn = "Sample_Type",
consumersColumn = "Species", groupsColumn = "Community",
d13C = "d13C", d15N = "d15N")
for (community in everglades.list) {
print(summary(community))
plot(community)
}
Summary for stable isotope data object
Group TSL-NP-Dry
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 15 3.3 0.5 0.1 2.7 4.2 3.1
dCb1 15 -25 1 0.2 -26.7 -23.4 -25.3
dNb2 20 3.9 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.3 0.3 -34.8 -30.5 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 4.7 2.6 0.9 0.5 7.9 4.9
dCc 8 -30.8 0.5 0.2 -31.4 -30.3 -30.6
Summary for stable isotope data object
Group TSL-NP-Dry
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 15 3.3 0.5 0.1 2.7 4.2 3.1
dCb1 15 -25 1 0.2 -26.7 -23.4 -25.3
dNb2 20 3.9 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.3 0.3 -34.8 -30.5 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 4.5 3.1 1 -0.4 7.7 5.4
dCc 9 -29.8 2.4 0.8 -31.4 -25.8 -31.4
Summary for stable isotope data object
Group TSL-NP-Dry
Consumer CLADOCERA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 15 3.3 0.5 0.1 2.7 4.2 3.1
dCb1 15 -25 1 0.2 -26.7 -23.4 -25.3
dNb2 20 3.9 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.3 0.3 -34.8 -30.5 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 3.1 1.2 0.6 2.1 4.1 3.1
dCc 4 -26.8 3.2 1.6 -29.5 -24.1 -26.8
Summary for stable isotope data object
Group TSL-NP-Dry
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 15 3.3 0.5 0.1 2.7 4.2 3.1
dCb1 15 -25 1 0.2 -26.7 -23.4 -25.3
dNb2 20 3.9 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.3 0.3 -34.8 -30.5 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.8 0.2 0.1 4.5 4.9 4.9
dCc 3 -30.5 2 1.1 -32.8 -29.4 -29.4
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 73 2.7 1.6 0.2 0.7 5.4 2.6
dCc 73 -28.2 1.3 0.2 -31.8 -26.3 -28
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3.1 0.6 0.2 2.3 3.7 3.1
dCc 6 -31.8 0.3 0.1 -32.2 -31.4 -31.7
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 6.3 1.6 0.6 4.7 7.9 6.4
dCc 8 -31.1 1.4 0.5 -32.4 -28.9 -31.5
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer BRAGRA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.8 2.5 1.1 2.1 6.9 6.4
dCc 5 -30.1 0.2 0.1 -30.3 -29.8 -30
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 2 0.2 0.1 1.8 2.1 2
dCc 6 -31.3 0.2 0.1 -31.6 -31.2 -31.3
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CHIRON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 5.4 1.5 0.7 3.4 6.5 5.8
dCc 4 -29.1 2 1 -30.8 -26.9 -29.4
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CICBIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 7.8 1.2 0.4 5.7 8.9 7.9
dCc 9 -28.9 1.5 0.5 -30.6 -25.7 -29.3
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CICMAN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 10.4 0 0 10.4 10.4 10.4
dCc 3 -26.5 3.5 2 -28.8 -22.4 -28.2
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 8.1 1 0.3 6.1 10.5 8
dCc 13 -28.2 1.2 0.3 -31.3 -27 -28.1
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 3.9 0.3 0.2 3.6 4.4 3.8
dCc 4 -30.8 0.5 0.3 -31.3 -30.3 -30.8
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer COLEOA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 3.5 2.3 1.1 1 6.4 3.3
dCc 4 -28.9 1.7 0.8 -30.8 -27.2 -28.9
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 6.1 1.7 0.8 3.9 7.8 5.5
dCc 5 -31.5 3.5 1.6 -33.5 -25.2 -32.6
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer CORIXI
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 6.4 1.6 0.9 4.6 7.7 6.9
dCc 3 -31.7 0.9 0.5 -32.8 -30.9 -31.4
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 8.4 1.1 0.4 6.6 10 8.8
dCc 7 -28.7 0.9 0.3 -30.4 -27.9 -28.3
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer EPHEME
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 2.3 0.6 0.3 1.9 3.1 2
dCc 4 -29.9 1.6 0.8 -32 -28 -29.8
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer ERYSIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.1 1.6 0.6 2.1 6.5 3.8
dCc 6 -28 1.2 0.5 -30 -26.6 -27.8
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 7.8 0.6 0.2 6.6 8.8 7.9
dCc 14 -26.8 1.4 0.4 -30 -25.2 -26.7
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 49 7.7 1.2 0.2 6.1 10 7.6
dCc 49 -28.2 1 0.1 -30.3 -25.8 -28.2
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer GERRID
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 6.2 0.2 0.1 5.9 6.4 6.2
dCc 4 -29.4 0.4 0.2 -29.8 -28.9 -29.4
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 21 8.8 1.1 0.2 7.1 10.2 9.3
dCc 21 -29.5 1.1 0.2 -31.1 -26.7 -29.6
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 7.1 0.9 0.5 6 8 7.2
dCc 4 -28.9 0.7 0.3 -29.8 -28.4 -28.7
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 55 8.2 1.6 0.2 4.9 11 8.3
dCc 55 -28.8 1 0.1 -31.2 -27 -29.1
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer MONALB
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.9 1.3 0.5 7.7 10.7 8.8
dCc 6 -26.9 1 0.4 -28 -25.1 -27.1
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer NOTPET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.8 0.6 0.3 8.1 9.7 8.6
dCc 5 -28.8 1.3 0.6 -30.6 -27 -28.7
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 5.3 3.2 1.1 0.5 8.5 6.6
dCc 9 -25.4 2.3 0.8 -31.4 -23.6 -25
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 6.4 0.7 0.3 5.4 7.4 6.4
dCc 7 -29.5 0.4 0.2 -30.2 -29.2 -29.3
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.5 1.8 0.7 1.5 7.1 4.6
dCc 6 -33.3 3.3 1.3 -37.5 -30.2 -31.6
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer PEREPI
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3 2 0.8 0.4 5.2 3.8
dCc 6 -28.4 1.3 0.5 -30 -26.7 -28.2
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer PLASCA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 6.5 0.4 0.2 6 6.9 6.7
dCc 5 -31 1.3 0.6 -32.3 -29.7 -31.6
Summary for stable isotope data object
Group TSL-Marsh-Wet
Consumer UTRFOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 2 0.8 0.4 1 3.1 2
dCb1 5 -26.9 4.4 2 -30.3 -19.6 -28.9
dNb2 20 4 0.4 0.1 3.2 4.7 4
dCb2 20 -32.1 1.2 0.3 -34.3 -30.1 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 5 3.5 2 2.2 8.8 3.9
dCc 3 -29.5 2.5 1.4 -32.4 -27.8 -28.4
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 24 2.4 1.4 0.3 0.6 5.4 2.3
dCc 24 -30 1.5 0.3 -33.8 -28.3 -29.7
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 6.4 1 0.3 5.3 8.6 6.4
dCc 12 -32 1.3 0.4 -34 -30.5 -31.4
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer BRAGRA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 6 1 0.5 5 6.8 6
dCc 4 -30.2 0.5 0.3 -30.6 -29.4 -30.4
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 5.5 0.6 0.3 5 6.1 5.4
dCc 4 -29.9 0.2 0.1 -30.1 -29.5 -29.9
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CHIRON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 3.1 0 0 3.1 3.1 3.1
dCc 4 -31.3 0.4 0.2 -31.7 -31 -31.3
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CICBIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 7.8 1 0.3 6 9 8
dCc 11 -27.7 1.9 0.6 -30.5 -25.1 -27
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CICMAN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 7.5 1.3 0.6 6 8.6 7.6
dCc 4 -29.2 1.5 0.8 -30.6 -27.3 -29.4
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 9.2 0.8 0.3 7.8 10.1 9.5
dCc 6 -28.7 0.8 0.3 -29.5 -27.3 -28.9
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CLADOCERA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 3.4 1.7 0.8 2.2 6.4 3
dCc 5 -30.7 0.8 0.3 -31.6 -29.9 -31.1
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 6.9 0.2 0.1 6.6 7.1 6.9
dCc 3 -30 0.7 0.4 -30.5 -29.2 -30.2
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer COLEOA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3.2 0.3 0.1 2.9 3.6 3
dCc 6 -30.6 0.2 0.1 -30.9 -30.4 -30.6
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 4.2 0.4 0.1 3.7 4.9 4.2
dCc 10 -31.8 1 0.3 -33.5 -30.8 -31.5
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer CORING
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 6.8 1.1 0.7 6.2 8.1 6.2
dCc 3 -28.1 1.1 0.6 -29.3 -27.4 -27.5
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 6.5 0.2 0.1 6.3 6.8 6.4
dCc 4 -29 1.2 0.6 -29.6 -27.1 -29.5
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 7.1 1.5 0.8 5.2 8.9 7.2
dCc 4 -30.1 0.6 0.3 -30.8 -29.4 -30
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer ERYSIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3.6 1 0.4 2.8 5.4 3.3
dCc 6 -29.4 1.6 0.6 -31.1 -27.6 -29.4
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.4 0.9 0.4 7.8 9.7 8
dCc 4 -27.8 0.8 0.4 -28.5 -26.7 -27.9
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 15 7.4 0.9 0.2 5.8 9.3 7.4
dCc 15 -28 0.7 0.2 -28.9 -26.4 -28.1
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer GERRID
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 6.2 0.4 0.2 5.7 6.6 6.3
dCc 4 -30 0.6 0.3 -31 -29.6 -29.8
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 19 9.2 1 0.2 6.8 11 9.3
dCc 19 -29.9 1.1 0.2 -31.6 -28.1 -30.1
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 7.2 0.7 0.3 6.4 8.2 7
dCc 5 -28.2 2.1 0.9 -31.4 -26.1 -27.3
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 15 7.4 2.3 0.6 4.6 10.1 7.1
dCc 15 -29.9 0.9 0.2 -31.1 -28.1 -30
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 3.6 1.2 0.5 2.7 6.2 3
dCc 7 -22.4 1.2 0.4 -24.9 -21.5 -22
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer PACLON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 3.1 0.3 0.1 2.7 3.7 3.1
dCc 7 -31.1 0.6 0.2 -31.7 -29.8 -31.3
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.7 0.9 0.4 3.8 6 4.5
dCc 6 -29.4 0.8 0.3 -30.1 -28 -29.7
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.2 2.4 1.4 2.2 6.9 3.5
dCc 3 -30.7 1.4 0.8 -32.2 -29.8 -30
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer PLASCA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.7 1.7 1 2.7 5.9 5.5
dCc 3 -31.9 3.1 1.8 -33.9 -28.3 -33.5
Summary for stable isotope data object
Group TSL-NP-Wet
Consumer PONCOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 7 3.2 0.6 0.2 2.7 4.4 2.9
dCb1 7 -27.5 0.8 0.3 -28.2 -26 -27.9
dNb2 20 3.9 0.3 0.1 3.3 4.5 3.9
dCb2 20 -32 0.9 0.2 -34 -30.9 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.7 0.9 0.5 4 5.7 4.4
dCc 3 -27.8 0.5 0.3 -28.1 -27.2 -27.9
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 2.6 1.1 0.7 1.3 3.2 3.2
dCc 3 -30.4 2.4 1.4 -32.7 -27.9 -30.5
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer BRAGRA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.4 0.6 0.3 3.8 5 4.4
dCc 4 -30.7 2.3 1.2 -32.8 -28.6 -30.7
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 8.1 0.8 0.2 7.3 9.4 7.7
dCc 13 -28 1.5 0.4 -29.1 -24.2 -28.8
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.2 0.3 0.1 3.9 4.5 4.2
dCc 5 -31.3 0.6 0.3 -32 -30.3 -31.4
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 86 8.2 0.7 0.1 7.1 9.6 8.2
dCc 86 -27.7 1.6 0.2 -36.8 -24.7 -27.8
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.1 0.5 0.2 7.4 8.5 8.2
dCc 4 -27.1 0.3 0.1 -27.4 -26.8 -27.1
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer LABSIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 28 9.5 0.6 0.1 8.4 10.5 9.5
dCc 28 -29.3 1.1 0.2 -31.2 -27.3 -29
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer LEPMAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 6.8 0.3 0.1 6.3 7.1 6.9
dCc 6 -30 0.5 0.2 -30.6 -29.3 -30.1
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 8.2 0.1 0 8.1 8.2 8.2
dCc 3 -29.6 1.7 1 -31.4 -28 -29.3
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 68 7.8 1.4 0.2 5.6 11 7.6
dCc 68 -29.9 1.6 0.2 -33.1 -26.2 -29.8
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer MACSIA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 58 8.5 0.6 0.1 6.7 9.6 8.6
dCc 58 -27.3 1.2 0.2 -29.7 -24.4 -27.2
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer MICSAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 33 10.6 0.6 0.1 9.6 12 10.5
dCc 33 -27.8 1.7 0.3 -30 -21 -27.9
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer MONALB
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.4 0.9 0.4 8.6 10.9 9
dCc 5 -26.6 1.3 0.6 -27.6 -24.3 -26.9
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer NOTPET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 16 8.7 1 0.3 7.4 10.3 8.7
dCc 16 -29 1.2 0.3 -31.1 -27.3 -28.9
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 2.8 0.3 0.2 2.4 3.1 2.8
dCc 4 -24.3 0.4 0.2 -24.9 -24 -24.2
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 6.1 0.7 0.2 5.2 6.9 5.9
dCc 8 -27.8 0.4 0.1 -28.4 -27.2 -27.7
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 8.8 0.8 0.5 8.2 9.7 8.3
dCc 3 -32.7 0.9 0.5 -33.3 -31.7 -33.2
Summary for stable isotope data object
Group TSL-Pond-Wet
Consumer TILMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.4 0.8 0.3 2.6 4.5 3.1
dCb1 5 -26.7 2.3 1 -28.2 -22.6 -27.8
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.8
dCb2 20 -31.9 1.2 0.3 -34 -30 -32.2
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 7.6 1 0.3 6.1 9.3 7.1
dCc 14 -30.8 2.9 0.8 -33.8 -23.6 -31.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 24 3.3 1.7 0.3 0.7 6.3 2.8
dCc 24 -27.3 3.2 0.7 -31.9 -19.6 -27.6
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 5.6 2 0.6 2.8 8.5 5.2
dCc 10 -29.3 2.5 0.8 -31 -24.3 -30.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.9 0.5 0.2 4.2 5.8 4.9
dCc 6 -30.5 0.6 0.2 -31.6 -30.1 -30.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer CHIRON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 6.4 0.5 0.2 5.7 7 6.5
dCc 7 -30.6 0.4 0.2 -31.1 -29.8 -30.5
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer CLADOCERA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 16 4.3 1.6 0.4 1.9 6.9 3.9
dCc 16 -29.3 2.5 0.6 -32.4 -24.2 -29.7
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.5 0.6 0.3 4.8 6.5 5.3
dCc 5 -29.4 1.4 0.6 -30.6 -27.6 -29.9
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 4.6 1.2 0.4 3 6 4.6
dCc 8 -30.4 1.1 0.4 -31.7 -28.3 -30.5
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer CORIXI
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.2 0.4 0.2 3.6 4.6 4.3
dCc 4 -28.1 0.8 0.4 -28.9 -27.2 -28.1
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 9.5 1.5 0.5 7.7 11.3 9.5
dCc 10 -28.8 0.5 0.1 -29.6 -28 -28.9
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer EPHEME
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 5.2 1.2 0.6 3.7 6.5 5.3
dCc 4 -32.6 0.7 0.4 -33.7 -32.1 -32.4
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer ERYSIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 3.2 1.2 0.7 2.3 4.6 2.6
dCc 3 -30.3 1.9 1.1 -32 -28.2 -30.7
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9 1.2 0.5 7.9 10.4 8.4
dCc 5 -27.9 0.8 0.4 -28.9 -27.2 -27.4
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.2 1.1 0.6 7.6 9.8 7.6
dCc 4 -28.3 0 0 -28.3 -28.2 -28.3
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer GERRID
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.8 1.1 0.5 3.2 5.6 5.2
dCc 4 -29 1.3 0.7 -30.1 -27.5 -29.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 24 9.1 1.4 0.3 7.3 11.7 8.8
dCc 24 -28.9 0.9 0.2 -30.2 -27.5 -29.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 51 7.7 1 0.1 5.8 11.2 7.5
dCc 51 -29.1 0.7 0.1 -30.3 -27.4 -29.2
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 4.1 1 0.3 3.1 6.4 4
dCc 12 -26.9 3.9 1.1 -33.1 -22.3 -27.5
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer PACLON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 6.5 1.6 0.7 3.2 7.5 7
dCc 6 -29.8 0.8 0.3 -30.3 -28.2 -30.1
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 7.4 1.4 0.4 4.7 9.6 7.5
dCc 13 -28.5 0.9 0.2 -30.2 -27.3 -28.5
Summary for stable isotope data object
Group TSL-Marsh-Dry
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 6 2.6 0.6 0.3 1.7 3.6 2.7
dCb1 6 -26.5 2.6 1.1 -29.7 -22.7 -27.3
dNb2 20 3.9 0.4 0.1 3.2 4.6 3.9
dCb2 20 -31.8 1.2 0.3 -33.8 -29.8 -31.6
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 4.5 1.5 0.4 2.2 7.1 4.7
dCc 17 -32.1 1.1 0.3 -33.7 -28.9 -32.4
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 3.5 1.7 1 1.6 4.6 4.2
dCc 3 -29 1.8 1 -30.2 -27 -29.9
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 18 5.5 2.4 0.6 -0.2 8.2 5.7
dCc 18 -29.7 3.3 0.8 -33.1 -22.8 -30.9
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer CHIRON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.1 0.3 0.2 3.7 4.3 4.2
dCc 3 -31.2 0.9 0.5 -31.7 -30.1 -31.7
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 5.6 0.7 0.2 4.9 6.4 5.5
dCc 8 -32.8 1.9 0.7 -34.8 -30.4 -33.1
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 64 7.9 0.7 0.1 6.1 10.1 8
dCc 64 -28.4 1.4 0.2 -33.8 -25.7 -28.3
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 6.9 0.3 0.2 6.6 7.3 6.8
dCc 4 -29.1 0.2 0.1 -29.4 -28.9 -29.1
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 8.4 1.6 0.4 5.6 10.6 8.4
dCc 14 -29.6 0.9 0.2 -31.6 -28.4 -29.4
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer MELTUB
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 3.9 0.5 0.3 3.4 4.2 4.1
dCc 3 -28.7 1.6 0.9 -30.5 -27.3 -28.2
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer MENBER
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 9 1.3 0.7 7 9.8 9.6
dCc 4 -28.2 0.2 0.1 -28.5 -28 -28.1
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer NOTPET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 23 8.9 1.7 0.4 6.1 11.1 8.8
dCc 23 -29.4 0.6 0.1 -30.6 -28.4 -29.3
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 20 4.8 2.7 0.6 0.1 8.4 5
dCc 20 -28.1 4.4 1 -34.3 -21.2 -29.6
Summary for stable isotope data object
Group TSL-Pond-Dry
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 5 3.5 0.5 0.2 3 4 3.7
dCb1 5 -24.5 3.1 1.4 -28 -21.3 -22.8
dNb2 20 3.9 0.4 0.1 2.8 4.5 4
dCb2 20 -31.9 1 0.2 -33.8 -29.1 -31.9
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 9 1 0.4 7.2 10 9.2
dCc 6 -28.9 0.3 0.1 -29.1 -28.4 -28.9
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer AMENAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 11.1 1.2 0.6 9.4 12.5 11.3
dCc 4 -30.6 0.6 0.3 -31.3 -29.9 -30.6
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 20 3.2 1.2 0.3 -1.1 4.6 3.4
dCc 20 -28.5 1 0.2 -30.8 -26.8 -28.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 5 1.2 0.5 3.7 7.2 4.8
dCc 6 -31.1 0.7 0.3 -32.1 -30.3 -31
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.7 0.2 0.1 4.5 5 4.7
dCc 5 -32.1 0.2 0.1 -32.4 -32 -32.1
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer CHIRON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 1.2 1.3 0.7 0.5 2.7 0.5
dCc 3 -30 2.4 1.4 -31.4 -27.3 -31.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 16 8.5 0.7 0.2 7 9.4 8.6
dCc 16 -29.9 2.3 0.6 -32.7 -26.9 -31.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer CLABAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 9.7 1.2 0.3 8.1 11.4 10
dCc 12 -30.6 1.5 0.4 -32.2 -26.8 -30.6
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 4.7 0.3 0.1 4.4 5.2 4.6
dCc 7 -33.8 0.9 0.3 -34.3 -31.9 -34.1
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 3.6 1.6 0.8 2.2 5.9 3.1
dCc 4 -32 0.6 0.3 -32.4 -31.2 -32.2
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 7.7 0.5 0.2 7 8.2 7.7
dCc 6 -31.4 0.6 0.2 -32.5 -30.8 -31.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.4 0.5 0.2 9 10.2 9.2
dCc 5 -31.8 1.3 0.6 -33.1 -29.9 -32.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.6 1.3 0.7 6.6 9.4 9.3
dCc 4 -31.3 1 0.5 -31.9 -29.8 -31.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 28 9 0.8 0.1 7.4 10 9.2
dCc 28 -28.5 1.4 0.3 -30.6 -25.8 -28.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 60 8.7 0.6 0.1 7.6 10.1 8.7
dCc 60 -30.2 0.7 0.1 -31.9 -28.9 -30.2
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer HEMLET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 43 9 0.8 0.1 7.4 10.2 9.1
dCc 43 -30.4 1.1 0.2 -33.6 -27.8 -30.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 9.2 0.5 0.1 7.9 9.8 9.3
dCc 14 -31 1.1 0.3 -32.7 -28.9 -31.2
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer JORFLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 7.8 1.6 0.5 5 9.2 8.7
dCc 11 -32.2 1 0.3 -33.8 -30.8 -32.2
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LEPMAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 19 10 0.9 0.2 8.6 11.4 9.9
dCc 19 -29.8 0.9 0.2 -31.1 -27.9 -29.8
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 7.7 0.8 0.3 7 9.3 7.5
dCc 6 -29.3 1.5 0.6 -30.5 -26.4 -29.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 9.6 0.8 0.3 8.3 10.7 9.5
dCc 8 -31.6 2.2 0.8 -33.8 -27.8 -32.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 10.2 0.8 0.4 8.8 10.9 10.6
dCc 5 -30.6 1.1 0.5 -31.6 -28.9 -31.1
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LIBINC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 2.1 0.2 0.1 2 2.3 2.1
dCc 3 -31.1 0 0 -31.2 -31.1 -31.1
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 74 8.2 1.1 0.1 4.7 10.2 8.4
dCc 74 -30.8 1.5 0.2 -33.5 -26.9 -30.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer MACSIA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 8.1 0.6 0.3 7.4 8.4 8.4
dCc 3 -29.3 0.6 0.3 -29.9 -28.7 -29.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer MELTUB
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.5 0.2 0.1 4.2 4.7 4.6
dCc 4 -28.2 1 0.5 -29.5 -27 -28.1
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer OREAUR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 21 8.9 0.8 0.2 7.4 10.6 8.9
dCc 21 -35.2 2 0.4 -38.2 -29.3 -35.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 7.3 0.9 0.3 6.2 8.8 6.9
dCc 11 -31.3 0.6 0.2 -31.9 -29.8 -31.3
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3.6 1 0.4 2.5 5.1 3.3
dCc 6 -30 1.4 0.6 -31 -27.5 -30.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer PERMAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 2.7 1.2 0.7 1.4 3.6 3
dCc 3 -27 1.9 1.1 -28.4 -24.9 -27.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 8.7 0.2 0.1 8.3 9 8.7
dCc 9 -32.9 0.9 0.3 -34.5 -31.6 -32.4
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.7 1 0.4 3.2 5.8 4.8
dCc 5 -29.6 0.5 0.2 -30.1 -28.8 -29.7
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer PROSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 5.3 0.4 0.2 4.8 5.8 5.4
dCc 4 -31.9 0.7 0.4 -32.8 -30.9 -31.9
Summary for stable isotope data object
Group SRS-Marsh-Wet
Consumer SNOOK
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 10.9 0.4 0.1 10.2 11.3 11
dCc 9 -28.7 0.2 0.1 -28.9 -28.4 -28.7
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.9 0.6 0.3 5.1 6.5 6.1
dCc 5 -33.2 1.2 0.5 -34.3 -31.2 -33.5
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 6.7 0.3 0.1 6.5 7 6.5
dCc 5 -32.7 0.7 0.3 -33.8 -32 -32.4
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 23 9 0.8 0.2 7.3 10.2 9
dCc 23 -31.6 0.6 0.1 -32.8 -30.5 -31.5
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.1 0.6 0.3 8.4 9.9 9.2
dCc 5 -29.1 0.9 0.4 -30 -27.8 -29.5
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 35 9.1 0.5 0.1 7.9 9.7 9.2
dCc 35 -29.9 0.6 0.1 -31 -28.8 -30
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer HEMLET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9.2 0.9 0.5 8.2 9.8 9.6
dCc 3 -30.3 0.7 0.4 -30.8 -29.5 -30.7
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 15 9.6 0.5 0.1 8.2 10.2 9.7
dCc 15 -31.6 1.4 0.4 -33.4 -28.3 -31.6
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 9 0.8 0.3 8 9.6 9.5
dCc 6 -30.3 0.6 0.3 -31.1 -29.5 -30.4
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 3.6 1.8 0.8 2.2 6.4 2.4
dCc 5 -30 1.8 0.8 -33.1 -28.5 -29.4
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 7.1 0.5 0.2 6.6 8.1 7
dCc 6 -30.9 0.3 0.1 -31.6 -30.7 -30.8
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.5 0.2 0.1 4.2 4.9 4.5
dCc 6 -31.6 0.5 0.2 -32 -30.5 -31.7
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 5.6 0.4 0.2 5.3 6.3 5.5
dCc 6 -29.9 0.9 0.4 -31.8 -29.1 -29.7
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer PROSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 6 0.3 0.1 5.8 6.4 5.9
dCc 6 -30.8 0.5 0.2 -31.3 -30.1 -31
Summary for stable isotope data object
Group SRS-NP-Wet
Consumer SAGLAN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 3.2 0.4 0.2 2.9 3.7 3.1
dCb1 4 -28.7 0.1 0.1 -28.9 -28.6 -28.8
dNb2 20 3.9 0.3 0.1 3.2 4.3 3.9
dCb2 20 -32 1.2 0.3 -34.6 -29.3 -32.3
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9 0.2 0.1 8.7 9.1 9.1
dCc 3 -26.4 0.4 0.2 -26.8 -26.1 -26.1
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer ACRIPT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.4 1.3 0.6 3.5 6.2 4.1
dCc 4 -32.3 1.4 0.7 -34.3 -31.3 -31.7
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer AMENAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9.3 0.3 0.2 9 9.6 9.2
dCc 3 -29.2 0.1 0.1 -29.2 -29.1 -29.2
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer AMICAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 11 0.7 0.3 10.1 11.7 11.2
dCc 4 -30.6 1.2 0.6 -32.1 -29.2 -30.6
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 4.4 1.1 0.3 2 6 4.5
dCc 13 -28.7 1.3 0.4 -31.4 -26.1 -28.6
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer BELBEL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 11 0.5 0.3 10.6 11.5 10.8
dCc 3 -29.3 0.5 0.3 -29.9 -28.8 -29.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer BRAGRA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 4.7 1.4 0.4 3.2 7.1 4.7
dCc 10 -31.8 0.9 0.3 -32.7 -30.6 -32
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 5.5 0.4 0.2 4.7 6 5.5
dCc 8 -32.2 0.9 0.3 -33.5 -30.9 -32.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 8.2 0.4 0.1 7.4 8.9 8.1
dCc 11 -29.6 1.7 0.5 -31.7 -25.7 -29.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer CLABAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9.2 1.3 0.8 7.7 10.3 9.5
dCc 3 -29.8 0.3 0.2 -30.1 -29.5 -29.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer CLAJAM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.9 2.6 1.5 1.9 6.4 6.4
dCc 3 -27.2 0.6 0.3 -27.6 -26.5 -27.4
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer COENAG
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.6 1.3 0.6 4.2 7.4 5.9
dCc 5 -33.3 1.3 0.6 -34.6 -32 -32.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 2.9 1.1 0.3 1.2 6.1 2.9
dCc 17 -30.5 1.9 0.5 -33.4 -27.8 -29.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer CORING
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 7.7 0.9 0.5 6.9 8.7 7.7
dCc 3 -30.3 0.8 0.5 -31.3 -29.6 -30.1
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer ELECEL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 -2.5 5.4 3.1 -5.9 3.7 -5.2
dCc 3 -26.8 0.3 0.2 -27.2 -26.6 -26.7
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 15 8.8 0.7 0.2 7.2 9.6 8.8
dCc 15 -30.6 0.5 0.1 -31.4 -29.4 -30.6
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 7.8 0.9 0.2 6.7 9.7 7.6
dCc 13 -30.7 0.7 0.2 -31.6 -29.8 -30.5
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 37 9.6 0.9 0.1 7.9 11.2 9.7
dCc 37 -28.5 2 0.3 -31.2 -23.7 -28.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer FUNCON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 8.3 0.4 0.1 7.4 8.7 8.3
dCc 9 -28.2 0.9 0.3 -30 -27.2 -28
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 178 9.2 0.6 0 7.7 10.9 9.2
dCc 178 -29.3 1.2 0.1 -32.3 -26.4 -29.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer HEMLET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 23 8.7 0.9 0.2 6.6 10.4 8.7
dCc 23 -30.8 1 0.2 -32.4 -28.4 -30.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 9.9 0.8 0.2 8.6 10.8 10.1
dCc 14 -29.9 3.5 0.9 -33.3 -23.7 -31.7
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer JORFLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.9 0.4 0.2 8.4 9.4 8.8
dCc 6 -30.5 0.8 0.3 -31.9 -29.6 -30.2
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPGUL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 39 9.6 0.4 0.1 8.5 10.3 9.6
dCc 39 -29.8 1 0.2 -32.2 -27.6 -29.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPMAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 8.3 0.9 0.3 7.4 9.9 8.1
dCc 8 -30.1 1 0.3 -31.3 -28.9 -30
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 8 0.9 0.3 6.7 10 8.1
dCc 12 -30.8 1.4 0.4 -32.8 -28.5 -31
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 15 8 0.7 0.2 6.5 8.9 8.1
dCc 15 -30.6 0.9 0.2 -32 -29.1 -30.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPPLA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 22 11.6 0.9 0.2 10 13.2 11.7
dCc 22 -29.9 1.3 0.3 -32 -27.8 -29.6
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 9.6 0.5 0.2 8.9 10.6 9.5
dCc 10 -29.9 0.9 0.3 -30.9 -28.3 -30.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LIBINC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.4 0.8 0.4 3.2 4.9 4.7
dCc 4 -29.9 0.8 0.4 -30.7 -29.2 -29.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 141 8.9 1 0.1 6.4 11.2 8.9
dCc 141 -30.2 1.6 0.1 -34.3 -24.9 -30.4
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer MACSIA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 29 8.1 0.8 0.1 6.9 9.5 8.1
dCc 29 -28.6 1.1 0.2 -30.6 -25 -28.6
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer MICSAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 18 11.7 0.5 0.1 11 12.6 11.6
dCc 18 -30 1.1 0.3 -32.1 -28.1 -29.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer MONALB
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.5 0.6 0.3 7.6 8.8 8.7
dCc 4 -29.7 0.9 0.4 -30.6 -28.5 -29.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer NOTPET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 10.5 0.4 0.2 10 10.8 10.4
dCc 5 -30.9 0.8 0.4 -31.3 -29.5 -31.2
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer NOTVIR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.2 0.4 0.2 7.8 8.7 8.3
dCc 4 -28.4 1.5 0.7 -30.1 -26.5 -28.4
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer OREAUR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.6 0.9 0.4 7.2 9.8 8.6
dCc 6 -35.2 2.4 1 -36.7 -30.5 -36
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PACLON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 5.3 0.8 0.5 4.5 6 5.5
dCc 3 -30.7 0.4 0.2 -31.1 -30.2 -30.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9.1 0.1 0.1 9 9.2 9.1
dCc 3 -31.4 1.2 0.7 -32.1 -30 -32
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 3.4 1.2 0.5 1.9 5 2.9
dCc 5 -29.7 1.6 0.7 -32.2 -28.5 -28.9
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PEREPI
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 2.9 1.3 0.6 1.7 5.1 2.4
dCc 5 -29 1.5 0.7 -30.7 -27.3 -28.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 16 9 0.6 0.2 8.2 10.8 8.9
dCc 16 -32.6 1.4 0.3 -34.5 -30 -32.5
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PROALL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 5.5 0.4 0.1 4.9 6.2 5.5
dCc 17 -28.4 0.8 0.2 -29.5 -26.7 -28.4
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.3 0.5 0.3 3.8 5.1 4.2
dCc 4 -28.7 1.3 0.6 -29.8 -27.3 -28.8
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer PROSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 5.4 0 0 5.4 5.4 5.4
dCc 3 -30.2 0.4 0.2 -30.5 -29.8 -30.3
Summary for stable isotope data object
Group SRS-Pond-Wet
Consumer TILMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 12 3.4 1 0.3 1.8 4.6 3.4
dCb1 12 -28.2 1.1 0.3 -30.7 -26.2 -28.2
dNb2 20 4 0.5 0.1 2.8 4.8 3.9
dCb2 20 -31.9 1.1 0.2 -33.7 -29.8 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 7.9 0.4 0.2 7.2 8.3 7.9
dCc 5 -31 0.5 0.2 -31.5 -30.5 -31
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 5 0.1 0 4.9 5.1 5
dCc 8 -26.8 0.2 0.1 -27.1 -26.6 -26.8
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer APHSAY
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 8.9 1.8 0.6 7 11.9 9.3
dCc 9 -30.1 0.5 0.2 -31.1 -29.6 -30
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer BELBEL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 10.2 0.7 0.4 9.7 11.1 9.9
dCc 3 -29.9 0.4 0.2 -30.2 -29.5 -30
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3 0.6 0.2 2.4 3.9 2.9
dCc 6 -29.3 0.5 0.2 -29.9 -28.6 -29.4
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.9 0.5 0.3 8.3 9.6 8.8
dCc 4 -31 1.2 0.6 -32.6 -29.9 -30.7
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 8.9 0.6 0.2 8.1 9.7 8.9
dCc 9 -30.1 1.1 0.4 -31.8 -28.5 -30.5
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 8.2 1 0.3 6.2 9.4 8.6
dCc 13 -31.9 0.9 0.3 -33.4 -31 -31.4
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.2 0.8 0.3 7.6 9.8 7.9
dCc 6 -31.9 0.4 0.2 -32.3 -31.2 -31.9
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer ERYSIM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 4.8 0.4 0.2 4.2 5.2 4.8
dCc 6 -27.7 0.6 0.3 -28.3 -26.9 -27.7
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 22 8.7 0.9 0.2 7 10.1 8.6
dCc 22 -28.4 1.6 0.3 -30.7 -25.6 -28.6
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer FUNCON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9 1.2 0.7 7.9 10.3 8.8
dCc 3 -30.3 1.2 0.7 -31.3 -29 -30.4
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 39 8.2 0.8 0.1 6.3 10.2 8.1
dCc 39 -31 1 0.2 -33.1 -29.3 -31.1
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer HEMLET
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 47 9.1 0.7 0.1 7.5 10.5 8.9
dCc 47 -31 1 0.1 -33 -29.3 -31
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 8.5 0.6 0.2 7.5 9.5 8.5
dCc 10 -31.5 1.4 0.4 -33.2 -29.1 -31.6
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer HOPLIT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.9 1 0.4 7.7 10.3 9
dCc 5 -32.9 1 0.5 -34 -31.6 -33.1
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer JORFLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8 0.6 0.3 7.2 8.5 8.2
dCc 4 -32.4 1.6 0.8 -34.6 -31.1 -31.9
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 8.8 0.9 0.3 7.5 10.1 8.6
dCc 7 -31.3 0.8 0.3 -32.2 -30 -31.3
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 9.2 0.6 0.2 8.6 10.5 9.2
dCc 13 -31 1.1 0.3 -32.6 -28.9 -30.8
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 50 7.8 0.8 0.1 5.7 9.4 7.8
dCc 50 -30.5 1.5 0.2 -33 -27.4 -30.8
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer NERFLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 9.5 0.9 0.4 8.2 10.2 9.7
dCc 4 -30.8 1.9 0.9 -33.5 -29.2 -30.3
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer NOTGYR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 8.6 0.1 0 8.5 8.6 8.6
dCc 3 -33.2 1.6 0.9 -34.5 -31.5 -33.7
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer NOTVIR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 8 1.2 0.7 6.6 8.9 8.3
dCc 3 -30.9 1.6 0.9 -32.7 -29.8 -30.2
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 6.9 0.4 0.2 6.5 7.6 6.9
dCc 6 -30.7 0.4 0.2 -30.9 -29.9 -30.8
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer PELFEM
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 3.8 0.6 0.2 2.8 5 3.9
dCc 17 -30 0.9 0.2 -31.5 -28.5 -29.7
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer PERMAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 4.7 0.6 0.3 4.3 5.6 4.5
dCc 4 -27.1 0.7 0.3 -27.9 -26.6 -27
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer PLASCA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4 0.5 0.3 3.6 4.6 3.8
dCc 3 -29.2 4.6 2.7 -32.8 -23.9 -30.8
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer PROALL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 5.1 0.8 0.3 3.2 5.8 5.2
dCc 9 -29.4 0.9 0.3 -30.4 -27.9 -29.6
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer RANATP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 4.2 0.6 0.2 3.4 5.3 4.3
dCc 9 -33.5 1.6 0.5 -35.2 -29.9 -33.9
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer SIRLAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 7.7 1.1 0.4 6.3 9.1 7.6
dCc 8 -30.4 0.8 0.3 -31.7 -29.4 -30.7
Summary for stable isotope data object
Group SRS-Marsh-Dry
Consumer TILMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 3 3.3 0.4 0.2 3.1 3.7 3.2
dCb1 3 -28.8 0.8 0.5 -29.5 -27.9 -29.1
dNb2 20 3.9 0.4 0.1 3.3 4.9 3.9
dCb2 20 -32.2 1.4 0.3 -34.9 -29 -32.1
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 6.7 1.7 0.7 4 8.5 7
dCc 5 -32.3 1.1 0.5 -33.7 -31.4 -31.7
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 8.6 0.8 0.3 7.4 10.2 8.5
dCc 8 -30.2 1.3 0.5 -32.3 -28.9 -29.6
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer ENNGLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 7.5 1.1 0.4 6.6 9.2 6.9
dCc 7 -32 0.9 0.4 -32.9 -30.5 -32.5
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 9.1 1.1 0.3 7.6 10.9 9
dCc 17 -29 1.5 0.4 -31.1 -24.9 -29.1
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 38 8.6 0.7 0.1 7.2 9.9 8.4
dCc 38 -31.1 1.3 0.2 -34.4 -29.4 -30.8
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 10 8 0.4 0.1 7.4 8.8 7.8
dCc 10 -30.9 1.6 0.5 -34.4 -29.1 -30.5
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.5 0.9 0.3 7.3 9.3 8.7
dCc 6 -30.5 1 0.4 -31.4 -28.6 -30.7
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 30 7.5 0.8 0.2 6.1 9.6 7.4
dCc 30 -31 1.1 0.2 -33 -28.3 -31.3
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 7 0.4 0.1 6.3 7.3 7.1
dCc 6 -30.3 0.6 0.2 -30.8 -29.2 -30.4
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 7.3 0.9 0.4 6 8.9 7.3
dCc 7 -33.3 1 0.4 -34.6 -32.1 -33.3
Summary for stable isotope data object
Group SRS-NP-Dry
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 4 0.3 0.2 3.7 4.5 4
dCb1 4 -28.1 0.2 0.1 -28.3 -28 -28.1
dNb2 20 3.9 0.4 0.1 3.1 4.6 3.9
dCb2 20 -31.8 0.9 0.2 -33.3 -30 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 4.7 0.3 0.1 4.2 5.1 4.8
dCc 8 -29.1 1 0.3 -30 -27.2 -29.3
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer ALGBAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 5.7 2 1.2 3.5 7.5 6.1
dCc 3 -27.6 1.4 0.8 -29.2 -26.5 -27.2
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer AMENAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 26 9.9 0.5 0.1 9.1 11.1 9.8
dCc 26 -30 0.7 0.1 -31.5 -28.8 -29.9
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer AMPHIPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 4.4 0.5 0.2 3.2 5.1 4.5
dCc 12 -27.2 1.4 0.4 -31 -25.6 -27.3
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer AMPMEA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 9.6 0.6 0.3 9.2 10.2 9.3
dCc 3 -28.1 0.9 0.5 -28.8 -27.1 -28.5
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer BELBEL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 10.5 0.4 0.2 10.2 11 10.4
dCc 4 -29.6 0.5 0.2 -30.1 -29.1 -29.5
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 5.4 0.2 0.1 5.2 5.6 5.4
dCc 3 -29.6 1.5 0.8 -30.5 -27.9 -30.4
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer BLUE MITE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 4.8 2.8 0.8 -1.1 7.9 5.8
dCc 13 -32.1 1.8 0.5 -35.1 -29.3 -31.7
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer BRAGRA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 6 1.1 0.3 4.2 7.5 6.2
dCc 11 -31.1 0.8 0.2 -32.1 -29.8 -31.1
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer CELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 3.1 0.6 0.3 2.5 4 2.9
dCc 6 -29.3 0.5 0.2 -29.8 -28.5 -29.6
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 19 7.9 0.5 0.1 7.2 8.7 7.9
dCc 19 -30.6 1.4 0.3 -35.8 -28.9 -30.3
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer CLADOCERA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 5.5 0.8 0.3 4.9 6.9 5.3
dCc 6 -30.7 2.9 1.2 -33.7 -27 -32
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer COPEPOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 23 5.9 1.2 0.2 2 7.7 5.7
dCc 23 -32.3 2.6 0.5 -35.4 -26.4 -32.9
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer ELAEVE
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.1 1 0.4 6.7 9.1 8.4
dCc 5 -30.6 1.1 0.5 -31.9 -29.7 -29.9
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer EPHEME
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 3 4.9 0.9 0.5 3.9 5.4 5.4
dCc 3 -32.8 0.4 0.2 -33 -32.3 -33
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 8.6 0.9 0.4 7.9 10.1 8.1
dCc 6 -30.8 1 0.4 -32.2 -29.6 -30.5
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 23 9.5 1 0.2 7.5 11.6 9.3
dCc 23 -28.3 1.7 0.4 -30.9 -25 -28.1
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer FUNCON
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 8.1 0.4 0.1 7.1 8.6 8.2
dCc 17 -29.6 0.7 0.2 -30.5 -28.4 -29.6
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 50 9.2 1 0.1 7 11 9.4
dCc 50 -29.5 1.7 0.2 -33.2 -25.9 -29.6
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 18 9.9 0.6 0.2 8.2 10.7 10
dCc 18 -31.4 1.2 0.3 -32.2 -27.5 -31.7
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer HOPLIT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 8.6 0.6 0.2 7.5 9.3 8.7
dCc 13 -31.1 1.1 0.3 -33 -29.1 -31.1
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer JORFLO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 12 8.1 0.8 0.2 6.5 9.3 8.3
dCc 12 -30.7 0.8 0.2 -32 -29.2 -30.7
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LEPGUL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 37 9.8 0.4 0.1 9.2 10.8 9.7
dCc 37 -29.8 1 0.2 -32.2 -27.8 -29.8
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LEPMAR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 13 8.7 0.9 0.2 7.3 9.9 9.1
dCc 13 -30.2 0.7 0.2 -31.4 -29.3 -30.1
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 7.5 0.5 0.3 6.7 7.9 7.8
dCc 4 -29.2 1 0.5 -30.1 -28.1 -29.2
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LEPPLA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 25 11.7 0.6 0.1 10.3 12.8 11.8
dCc 25 -30.3 1.3 0.3 -32.3 -27.9 -30.4
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 9.1 0.5 0.2 8.1 9.7 9.2
dCc 9 -30.1 1.1 0.4 -32.4 -28.8 -29.8
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer LUCGOO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 57 8.5 0.9 0.1 5.8 11.1 8.4
dCc 57 -30.8 1.3 0.2 -32.5 -26.3 -31
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer OSTRACOD
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 31 5 1.5 0.3 2 6.9 5.6
dCc 31 -28.2 2.5 0.5 -34 -24.3 -27.2
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 7.6 0.5 0.2 7 8.4 7.6
dCc 6 -31.3 0.5 0.2 -32 -30.5 -31.3
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer PLASCA
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 5.8 1.6 0.5 3.6 7.7 6.7
dCc 9 -30.9 0.9 0.3 -31.9 -28.9 -31.2
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 8.2 0.9 0.3 6.6 9.7 8.2
dCc 11 -33.1 1.3 0.4 -35.1 -31.2 -32.6
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer PROALL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 4.4 0.7 0.2 2.8 5 4.6
dCc 8 -29.3 0.7 0.3 -30.7 -28.5 -29.1
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 17 4.5 0.5 0.1 3.8 5.5 4.4
dCc 17 -28 1 0.2 -29.8 -26.6 -28
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer RANGRY
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 5.8 0.6 0.2 5.2 6.7 5.7
dCc 6 -30.4 1.5 0.6 -32.4 -28.5 -30.4
Summary for stable isotope data object
Group SRS-Pond-Dry
Consumer SIRLAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 11 3.4 1.1 0.3 1.7 4.6 3.5
dCb1 11 -28.8 1.2 0.4 -30.9 -27.2 -28.4
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.9
dCb2 20 -32.3 1.3 0.3 -34.2 -29.9 -32.5
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 7.5 1 0.4 5.8 8.8 7.6
dCc 7 -30.5 1.5 0.6 -32 -28.4 -30.9 
everglades.models <- multiSpeciesTP(everglades.list, model = "twoBaselinesFull",
n.adapt = 10000, n.iter = 10000,
burnin = 1000, thin = 10, chains = 2, print = F)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 32
Total graph size: 306
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 34
Total graph size: 310
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 24
Total graph size: 290
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 22
Total graph size: 286
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 359
Unobserved stochastic nodes: 162
Total graph size: 546
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 32
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 34
Total graph size: 290
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 42
Total graph size: 306
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
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|
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|+++++++++++ | 22%
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
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|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 30
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 44
Total graph size: 310
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
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|
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|*** | 6%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 311
Unobserved stochastic nodes: 114
Total graph size: 450
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|************************************************* | 98%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 255
Unobserved stochastic nodes: 58
Total graph size: 338
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|++++++++ | 16%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 323
Unobserved stochastic nodes: 126
Total graph size: 474
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
|
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|
|++++++++++ | 20%
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
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|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
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|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
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|
|++++++++++++++++++++++++++++++++ | 64%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++ | 72%
|
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|
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|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 34
Total graph size: 290
Initializing model
|
| | 0%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 30
Total graph size: 282
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|++++ | 8%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 265
Unobserved stochastic nodes: 64
Total graph size: 354
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 40
Total graph size: 306
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
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|+ | 2%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 38
Total graph size: 302
Initializing model
|
| | 0%
|
|+ | 2%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 28
Total graph size: 282
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 26
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 22
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 28
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|******** | 16%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 36
Total graph size: 298
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
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|
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|
|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++ | 72%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
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|
|************ | 24%
|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
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|
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|
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|
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|
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|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 22
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
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|
|**************** | 32%
|
|***************** | 34%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 28
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 46
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 24
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 255
Unobserved stochastic nodes: 54
Total graph size: 334
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 26
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 46
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
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| | 0%
|
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|
|** | 4%
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|
|**** | 8%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 30
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|
|++++++++ | 16%
|
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|
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|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|
| | 0%
|
|* | 2%
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|** | 4%
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|****** | 12%
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 30
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
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|
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|
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|
|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++ | 72%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 28
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
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|
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|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
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|
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|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
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|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
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|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|*************************** | 54%
|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|
|************************************ | 72%
|
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|
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|
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|
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|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 22
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 22
Total graph size: 270
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 22
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++ | 16%
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 42
Total graph size: 306
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|
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|
|++++++++ | 16%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|++++++++++++ | 24%
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
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|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
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|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
|
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|
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|******************************** | 64%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 385
Unobserved stochastic nodes: 188
Total graph size: 598
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 269
Unobserved stochastic nodes: 72
Total graph size: 366
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|++++++++ | 16%
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
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|
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|
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|
| | 0%
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|** | 4%
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|*** | 6%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 349
Unobserved stochastic nodes: 152
Total graph size: 526
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
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|+++++++++++ | 22%
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|++++++++++++++++ | 32%
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|
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|
|++++++++++++++++++++++++ | 48%
|
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|
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|
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|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
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|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
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|
|++++++++++++++++++++++++++++++++++++ | 72%
|
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|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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|
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|
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|**************** | 32%
|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 329
Unobserved stochastic nodes: 132
Total graph size: 486
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
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|
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|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 279
Unobserved stochastic nodes: 82
Total graph size: 386
Initializing model
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 26
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 245
Unobserved stochastic nodes: 48
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 32
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|++++++++ | 16%
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
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|+++++++++++ | 22%
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
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|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
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|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
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|
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|******************************************* | 86%
|
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|
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 44
Total graph size: 310
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 263
Unobserved stochastic nodes: 64
Total graph size: 352
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 36
Total graph size: 296
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 28
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 30
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 48
Total graph size: 320
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 26
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 32
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 24
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 36
Total graph size: 296
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
|++++++++ | 16%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 24
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
|
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|
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 22
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++ | 70%
|
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|
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|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|
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|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 26
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
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|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
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|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
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|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
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|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
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|
|************ | 24%
|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 24
Total graph size: 272
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 24
Total graph size: 272
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 263
Unobserved stochastic nodes: 64
Total graph size: 352
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 317
Unobserved stochastic nodes: 118
Total graph size: 460
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 40
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 28
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 42
Total graph size: 308
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
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|
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|
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|
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|
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|
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|
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|
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|
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|++++++++++++++++++++++++++++++++++++ | 72%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
|
|**** | 8%
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|***** | 10%
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|****** | 12%
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 50
Total graph size: 324
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
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|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
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|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
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|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
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|
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|
|******** | 16%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************************** | 88%
|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 52
Total graph size: 326
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
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|
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|
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|
|++++++++++++++++ | 32%
|
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|
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|
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|
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|
|+++++++++++++++++++++ | 42%
|
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|
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|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
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|
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|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
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|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
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|
|*********** | 22%
|
|************ | 24%
|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|
|************************************ | 72%
|
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|
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|
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|
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|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 32
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
|*********************** | 46%
|
|************************ | 48%
|
|************************* | 50%
|
|************************** | 52%
|
|*************************** | 54%
|
|**************************** | 56%
|
|***************************** | 58%
|
|****************************** | 60%
|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 341
Unobserved stochastic nodes: 144
Total graph size: 510
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 44
Total graph size: 310
Initializing model
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 22
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 24
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 259
Unobserved stochastic nodes: 62
Total graph size: 346
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 56
Total graph size: 334
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 28
Total graph size: 278
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 56
Total graph size: 332
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
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|+ | 2%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
| | 0%
|
|+ | 2%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 48
Total graph size: 316
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 40
Total graph size: 300
Initializing model
|
| | 0%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 30
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|
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|
|++++ | 8%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 267
Unobserved stochastic nodes: 72
Total graph size: 364
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 331
Unobserved stochastic nodes: 136
Total graph size: 492
Initializing model
|
| | 0%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 297
Unobserved stochastic nodes: 102
Total graph size: 424
Initializing model
|
| | 0%
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|+ | 2%
|
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|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 44
Total graph size: 308
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 38
Total graph size: 296
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 54
Total graph size: 328
Initializing model
|
| | 0%
|
|+ | 2%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|++++++++ | 16%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 32
Total graph size: 284
Initializing model
|
| | 0%
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|
|++ | 4%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 359
Unobserved stochastic nodes: 164
Total graph size: 548
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|
|++++++++ | 16%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
| | 0%
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|*** | 6%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|******************************** | 64%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
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|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
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|
|******** | 16%
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|
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|
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|
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|
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|
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|
|**************** | 32%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 58
Total graph size: 336
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 38
Total graph size: 296
Initializing model
|
| | 0%
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|+ | 2%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 34
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 34
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|++++++ | 12%
|
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|
|++++++++ | 16%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
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|** | 4%
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|*** | 6%
|
|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|****** | 12%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 257
Unobserved stochastic nodes: 62
Total graph size: 344
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
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|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
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|
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|
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|
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|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
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|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
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|
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|
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|
|++++++++++++++++++++++++ | 48%
|
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|
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|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|+++++++++++++++++++++++++++++++ | 62%
|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
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|
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|
|************************************* | 74%
|
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|
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|
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|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 281
Unobserved stochastic nodes: 86
Total graph size: 392
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 46
Total graph size: 312
Initializing model
|
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
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|
|**************************************************| 100%
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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| | 0%
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|** | 4%
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|**** | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|**** | 8%
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|
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|
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|
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|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 22
Total graph size: 264
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
|
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|
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|
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|
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|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 24
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
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|
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|
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|
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|
|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
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|****** | 12%
|
|******* | 14%
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|******** | 16%
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|
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|
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|
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|
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|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
|******************************************* | 86%
|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
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|
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|
|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
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|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
|******************* | 38%
|
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|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
|*************************** | 54%
|
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|
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|
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|
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|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
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|
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|
|**************************************** | 80%
|
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|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 24
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
|************** | 28%
|
|*************** | 30%
|
|**************** | 32%
|
|***************** | 34%
|
|****************** | 36%
|
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|
|******************** | 40%
|
|********************* | 42%
|
|********************** | 44%
|
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|
|************************ | 48%
|
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|
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|
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|
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|
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|
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|
|******************************* | 62%
|
|******************************** | 64%
|
|********************************* | 66%
|
|********************************** | 68%
|
|*********************************** | 70%
|
|************************************ | 72%
|
|************************************* | 74%
|
|************************************** | 76%
|
|*************************************** | 78%
|
|**************************************** | 80%
|
|***************************************** | 82%
|
|****************************************** | 84%
|
|******************************************* | 86%
|
|******************************************** | 88%
|
|********************************************* | 90%
|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 42
Total graph size: 320
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
|+++++++++++++++++++ | 38%
|
|++++++++++++++++++++ | 40%
|
|+++++++++++++++++++++ | 42%
|
|++++++++++++++++++++++ | 44%
|
|+++++++++++++++++++++++ | 46%
|
|++++++++++++++++++++++++ | 48%
|
|+++++++++++++++++++++++++ | 50%
|
|++++++++++++++++++++++++++ | 52%
|
|+++++++++++++++++++++++++++ | 54%
|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
|++++++++++++++++++++++++++++++ | 60%
|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|++++++++++++++++++++++++++++++++++++++ | 76%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
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|++ | 4%
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|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 36
Total graph size: 308
Initializing model
|
| | 0%
|
|+ | 2%
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 32
Total graph size: 300
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 38
Total graph size: 312
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|******************************** | 64%
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
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|++++++++++++++++ | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
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|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
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|
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|
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|
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|
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|
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|
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|
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|******************************** | 64%
|
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|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 26
Total graph size: 288
Initializing model
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 261
Unobserved stochastic nodes: 50
Total graph size: 336
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 257
Unobserved stochastic nodes: 46
Total graph size: 328
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 42
Total graph size: 320
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 301
Unobserved stochastic nodes: 90
Total graph size: 416
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
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|
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 245
Unobserved stochastic nodes: 34
Total graph size: 304
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 583
Unobserved stochastic nodes: 372
Total graph size: 980
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 273
Unobserved stochastic nodes: 62
Total graph size: 360
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 255
Unobserved stochastic nodes: 44
Total graph size: 324
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 28
Total graph size: 292
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 305
Unobserved stochastic nodes: 94
Total graph size: 424
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 32
Total graph size: 300
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 40
Total graph size: 316
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 257
Unobserved stochastic nodes: 46
Total graph size: 328
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 271
Unobserved stochastic nodes: 60
Total graph size: 356
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 36
Total graph size: 308
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 24
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 509
Unobserved stochastic nodes: 298
Total graph size: 832
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 285
Unobserved stochastic nodes: 74
Total graph size: 384
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 263
Unobserved stochastic nodes: 52
Total graph size: 340
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 24
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 26
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 24
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|++++++++ | 16%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 28
Total graph size: 292
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|
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|
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|
|*** | 6%
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|**** | 8%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
| | 0%
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|++ | 4%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 26
Total graph size: 288
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 26
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 259
Unobserved stochastic nodes: 48
Total graph size: 332
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 261
Unobserved stochastic nodes: 50
Total graph size: 336
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++ | 16%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 22
Total graph size: 280
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 26
Total graph size: 288
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 32
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 34
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 215
Unobserved stochastic nodes: 22
Total graph size: 262
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 28
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|++++++++ | 16%
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|*** | 6%
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|**** | 8%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 24
Total graph size: 266
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 34
Total graph size: 286
Initializing model
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 42
Total graph size: 302
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 28
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 28
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 60
Total graph size: 338
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 215
Unobserved stochastic nodes: 22
Total graph size: 262
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|
|++++++++ | 16%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 287
Unobserved stochastic nodes: 94
Total graph size: 406
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 303
Unobserved stochastic nodes: 110
Total graph size: 438
Initializing model
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 36
Total graph size: 290
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 26
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 24
Total graph size: 266
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 30
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 42
Total graph size: 302
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 309
Unobserved stochastic nodes: 116
Total graph size: 450
Initializing model
|
| | 0%
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|++ | 4%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 24
Total graph size: 266
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 215
Unobserved stochastic nodes: 22
Total graph size: 262
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 215
Unobserved stochastic nodes: 22
Total graph size: 262
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 28
Total graph size: 274
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 50
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 217
Unobserved stochastic nodes: 24
Total graph size: 266
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 215
Unobserved stochastic nodes: 22
Total graph size: 262
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 34
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 34
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 32
Total graph size: 282
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++ | 16%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 26
Total graph size: 270
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
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|+++++++ | 14%
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|*** | 6%
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 32
Total graph size: 284
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 30
Total graph size: 280
Initializing model
|
| | 0%
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|+ | 2%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 245
Unobserved stochastic nodes: 50
Total graph size: 320
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 287
Unobserved stochastic nodes: 92
Total graph size: 404
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 36
Total graph size: 292
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 271
Unobserved stochastic nodes: 76
Total graph size: 372
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 225
Unobserved stochastic nodes: 30
Total graph size: 280
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 32
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 22
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
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|++++++++ | 16%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|** | 4%
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|*** | 6%
|**** | 8%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 277
Unobserved stochastic nodes: 68
Total graph size: 370
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|++++++++++ | 20%
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|
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|
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|
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|
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|
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|
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|
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|
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|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
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|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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| | 0%
|
|* | 2%
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|** | 4%
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|*** | 6%
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|**** | 8%
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 40
Total graph size: 314
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
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|+++++++++ | 18%
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|++++++++++ | 20%
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|++++++++++++++++++++++++++++++++++++++++ | 80%
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|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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| | 0%
|
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|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
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|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 22
Total graph size: 278
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 24
Total graph size: 282
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 22
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 42
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 38
Total graph size: 310
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 28
Total graph size: 290
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
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|
|************************************************* | 98%
|
|**************************************************| 100%Warning: Some HDRs are incompleteCompiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 263
Unobserved stochastic nodes: 54
Total graph size: 342
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 28
Total graph size: 290
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 271
Unobserved stochastic nodes: 62
Total graph size: 358
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 235
Unobserved stochastic nodes: 26
Total graph size: 286
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 231
Unobserved stochastic nodes: 22
Total graph size: 278
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 28
Total graph size: 290
Initializing model
|
| | 0%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|++++++++ | 16%
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|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 271
Unobserved stochastic nodes: 62
Total graph size: 358
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
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|+++++++++++ | 22%
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|++++++++++++++++ | 32%
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|
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|+++++++++++++++++++++++++++++++++++++++++++ | 86%
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|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
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|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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| | 0%
|
|* | 2%
|
|** | 4%
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|*** | 6%
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|**** | 8%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 259
Unobserved stochastic nodes: 50
Total graph size: 334
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 325
Unobserved stochastic nodes: 116
Total graph size: 466
Initializing model
|
| | 0%
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|+ | 2%
|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 261
Unobserved stochastic nodes: 52
Total graph size: 338
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
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|+++ | 6%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 42
Total graph size: 318
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 40
Total graph size: 314
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 299
Unobserved stochastic nodes: 90
Total graph size: 414
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 251
Unobserved stochastic nodes: 42
Total graph size: 318
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 24
Total graph size: 282
Initializing model
|
| | 0%
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 34
Total graph size: 302
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
|************************************************* | 98%Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 339
Unobserved stochastic nodes: 130
Total graph size: 494
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 287
Unobserved stochastic nodes: 78
Total graph size: 390
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|+++++ | 10%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 28
Total graph size: 290
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
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|++++++ | 12%
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|++++++++ | 16%
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|++++++++++ | 20%
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 34
Total graph size: 302
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 247
Unobserved stochastic nodes: 38
Total graph size: 310
Initializing model
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Compiling model graph
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Graph information:
Observed stochastic nodes: 241
Unobserved stochastic nodes: 32
Total graph size: 298
Initializing model
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Compiling model graph
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Graph information:
Observed stochastic nodes: 259
Unobserved stochastic nodes: 50
Total graph size: 334
Initializing model
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Compiling model graph
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Graph information:
Observed stochastic nodes: 237
Unobserved stochastic nodes: 28
Total graph size: 290
Initializing model
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Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 30
Total graph size: 294
Initializing model
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|**************************************************| 100%#plot communities
slough.habitat.season.tp.alpha <- credibilityIntervals(everglades.models$df, x = "group", xlab = "Community")
4.1 Extract model output
#trophic position
#extract modes and credible intervals
trophic.positions <- sapply(everglades.models$TPs, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>%
as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>%
separate(rowname, into = c("Slough", "Habitat", "Season", "Species"), sep = "\\.") %>%
arrange(Slough, Habitat, Season, `50%`)Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#write out to csv
write.csv(trophic.positions,
file = "Outputs/Stable Isotopes/Post-Invasion/trophic_position_mode_ci.csv",
row.names = F)
#pairwise comparisons of trophic position
pairwiseTP <- pairwiseComparisons(everglades.models$TPs) %>% as.data.frame() %>% rownames_to_column()
colnames(pairwiseTP) <- c("Species", pairwiseTP$rowname)
#write out to csv
write.csv(pairwiseTP,
row.names = F)
#alpha
#extract modes and credible intervals
alpha <- sapply(everglades.models$Alphas, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>%
as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>%
separate(rowname, into = c("Slough", "Habitat", "Season", "Species"), sep = "\\.") %>%
arrange(Slough, Habitat, Season, `50%`)Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].write.csv(alpha,
file = "Outputs/Stable Isotopes/Post-Invasion/alpha_mode_ci.csv",
row.names = F)
#pairwise comparisons
pairwisealpha <- pairwiseComparisons(everglades.models$Alphas) %>% as.data.frame() %>% rownames_to_column()
colnames(pairwisealpha) <- c("Species", pairwisealpha$rowname)
#write out to excel
write.csv(pairwisealpha,
file = "Outputs/Stable Isotopes/Post-Invasion/pairwise_alpha.csv",
row.names = F)4.2 Plot post-invasion trophic positions
Format data
#change Species names to full scientific names for lowest taxonomic level
alpha.mean.ci <- alpha %>% arrange(Species, Slough, Habitat, Season) %>% #select(Species, `2.5%`:`97.5%`) %>%
rename(alpha_2.5 = `2.5%`, alpha_50 = `50%`, alpha_97.5 = `97.5%`)
tp.alpha.plot <- trophic.positions %>% arrange(Species, Slough, Habitat, Season) %>%
rename(TP_2.5 = `2.5%`, TP_50 = `50%`, TP_97.5 = `97.5%`) %>%
bind_cols(select(alpha.mean.ci, alpha_2.5:alpha_97.5)) %>%
filter(!Species %in% c("CLAJAM", "ELECEL", "PEREPI", "PERMAT", "PONCOR", "SAGLAN", "UTRFOL")) %>%
mutate(Species = case_when(
Species == "ACRIPT" ~ "Acris gryllus - tadpole",
Species == "ALGBAL" ~ "Nostoc spp.",
Species == "AMENAT" ~ "Ameiurus natalis",
Species == "AMICAL" ~ "Amia calva",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "AMPMEA" ~ "Amphiuma means",
Species == "APHSAY" ~ "Aphredoderus sayanus",
Species == "BELBEL" ~ "Belonesox belizanus",
Species == "BELSPP" ~ "Belostoma spp.",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "Brachymesia gravida",
Species == "CELSPP" ~ "Celithemis spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "Cichlasoma bimaculatum",
Species == "CICMAN" ~ "Cichlasoma managuense",
Species == "CICURO" ~ "Mayaheros urophthalmus",
Species == "CLABAT" ~ "Clarias batrachus",
Species == "CLADOCERA" ~ "Cladocera",
#Species == "CLAJAM" ~ "C. jamaicense",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORING" ~ "Coryphaeschna ingens",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "Elassoma evergladei",
#Species == "ELECEL" ~ "E. cellulosa",
Species == "ENNGLO" ~ "Enneacanthus gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "Erimyzon sucetta",
Species == "ERYSIM" ~ "Erythemis simplicicollis",
Species == "FUNCHR" ~ "Fundulus chrysotus",
Species == "FUNCON" ~ "Fundulus confluentus",
Species == "GAMHOL" ~ "Gambusia holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "Rubrachatochromis letourneuxi",
Species == "HETFOR" ~ "Heterandria formosa",
Species == "HOPLIT" ~ "Hoplisternum littorale",
Species == "JORFLO" ~ "Jordanella floridae",
Species == "LABSIC" ~ "Labidesthes vanhyningi",
Species == "LEPGUL" ~ "Lepomis gulosus",
Species == "LEPMAC" ~ "Lepomis macrochirus",
Species == "LEPMAR" ~ "Lepomis marginatus",
Species == "LEPMIC" ~ "Lepomis microlophus",
Species == "LEPPLA" ~ "Lepisosteus platyrhincus",
Species == "LEPPUN" ~ "Lepomis punctatus",
Species == "LIBINC" ~ "Libellula incesta",
Species == "LUCGOO" ~ "Lucania goodei",
Species == "MACSIA" ~ "Macrognathus siamensis",
Species == "MELTUB" ~ "Melanoides tuberculata",
Species == "MENBER" ~ "Menidia berylina",
Species == "MICSAL" ~ "Micropterus salmoides",
Species == "MONALB" ~ "Monopterus albus/javaensis",
Species == "NERFLO" ~ "Nerodia floridana",
Species == "NOTGYR" ~ "Noturus gyrinus",
Species == "NOTPET" ~ "Notropis petersoni",
Species == "NOTVIR" ~ "Notophthalmus viridescens",
Species == "OREAUR" ~ "Oreochromis aureus",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PACLON" ~ "Pachydiplax longipennis",
Species == "PALPAL" ~ "Palaemonetes paludosus",
Species == "PELFEM" ~ "Pelocoris femoratus",
#Species == "PEREPI" ~ "Periphyton - Epiphytic",
#Species == "PERMAT" ~ "Periphyton - Mat",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "Poecilia latipinna",
#Species == "PONCOR" ~ "P. cordata",
Species == "PROALL" ~ "Procambarus alleni",
Species == "PROFAL" ~ "Procambarus fallax",
Species == "PROSPP" ~ "Procambarus juveniles",
Species == "RANATP" ~ "Rana grylio - tadpoles",
Species == "RANGRY" ~ "Rana grylio",
#Species == "SAGLAN" ~ "S. lancifolia",
Species == "SIRLAC" ~ "Sirens lacertina",
Species == "SNOOK" ~ "Centropomis undecimalis",
Species == "TILMAR" ~ "Pelmetolapia mariae"
#Species == "UTRFOL" ~ "U. foliosa"
)) %>%
mutate(Habitat = case_when(Habitat == "NP" ~ "Near Pond",
Habitat != "NP" ~ Habitat)) %>%
arrange(Slough, Season, Species, Habitat)Trophic position per species per habitat-season-slough
windowsFonts(Times = windowsFont("Times New Roman"))
(tp.current.plot <- ggplot(data = tp.alpha.plot, aes(x = Species, y = TP_50, fill = Season, color = Season, fill = Season))+
facet_grid(Habitat ~ Slough)+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
ylab("Trophic Position")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)Warning: Duplicated aesthetics after name standardisation: fillWarning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
Please use `linewidth` instead.Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
Please use the `linewidth` argument instead.
# ggsave(filename = "trophic_position_habitat_season_slough.tiff",
# plot = tp.current.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
4.2.1 Seasonal comparisons
#Format data
tp.seasonal.comparisons <- tp.alpha.plot %>%
filter(
#filter for species within a slough within a habtiat that have data from both seasons
Species %in% c(
"Mayaheros urophthalmus",
"Celithemis spp.",
"Elassoma evergladei",
"Enneacanthus gloriosus",
"Erimyzon sucetta",
"Fundulus chyrsotus",
"Gambusia holbrooki",
"Heterandria formosa",
"Anomalochromis letourneuxi",
"Jordanella floridae",
"Lucania goodei",
"Lepomis marginatus",
"Lepomis punctatus",
"Pelcoris femoratus",
"Palaemonetes paludosus"
) & Slough == "SRS" & Habitat == "Marsh" |
Species %in% c(
"Elassoma evergladei",
"Fundulus chrysotus",
"Gambusia holbrooki",
"Heterandria formosa",
"Lucania goodei",
"Procambarus fallax",
"Palaemonetes paludosus"
) &
Slough == "SRS" & Habitat == "Near Pond" |
Species %in% c(
"Ameiurus natalis",
"Amphipoda",
"Belonesox belizanus",
"Brachymesia gravida",
"Mayaheros urophthalmus",
"Celithemis spp.",
"Copepoda",
"Erimyzon sucetta",
"Fundulus chrysotus",
"Fundulus confluentus",
"Gambusia holbrooki",
"Heterandria formosa",
"Jordanella floridae",
"Lucania goodei",
"Lepomis gulosus",
"Lepomis marginatus",
"Lepomis microlophus",
"Lepisosteus platyrhincus",
"Lepomis punctatus",
"Procambarus alleni",
"Procambarus fallax",
"Poecilia latipinna",
"Palaemonetes paludosus"
) &
Slough == "SRS" & Habitat == "Pond" |
Species %in% c(
"Amphipoda",
"Celithemis spp.",
"Chironomidae",
"Coenagrionidae",
"Copepoda",
"Corixidae",
"Enneacanthus gloriosus",
"Erythemis simpliciollis",
"Ephemeroptera",
"Fundulus chrysotus",
"Gambusia holbrooki",
"Gerridae",
"Heterandria formosa",
"Hydrachnidia",
"Lucania goodei",
"Pelocoris femoratus",
"Palaemonetes paludosus"
) & Slough == "TSL" & Habitat == "Marsh" |
Species %in% c("Amphipoda", "Cladocera", "Copepoda", "Hydrachnidia") &
Slough == "TSL" & Habitat == "Near Pond" |
Species %in% c(
"Amphipoda",
"Copepoda",
"Gambusia holbrooki",
"Heterandria formosa",
"Lucania goodei",
"Notropis petersoni",
"Ostracoda",
"Palaemonetes paludosus"
) &
Slough == "TSL" & Habitat == "Pond"
)
#Plot
(tp.seasonal.comparisons.plot <- ggplot(data = tp.seasonal.comparisons %>%
mutate(Species = fct_reorder(Species, TP_50),
Season = fct_rev(Season)),
aes(x = Species, y = TP_50, fill = Season, color = Season))+
facet_grid(Habitat ~ Slough)+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(begin = 0.2, end = 0.88) +
scale_fill_viridis_d(begin = 0.2, end = 0.88) +
scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
ylab("Trophic Position")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(filename = "trophic_position_seasonal_comparisons.tiff",
# plot = tp.seasonal.comparisons.plot,
# width = 174, height = 60, units = "mm", dpi = 600)4.2.2 Habitat comparisons
#Format data
tp.habitat.comparisons <- tp.alpha.plot %>%
filter(
#filter for species within a slough within a season that have data from both seasons
Species %in% c(
"Belonesox belizanus",
"Mayaheros urophthalmus",
"Celithemis spp.",
"Elassoma evergladei",
"Enneacanthus gloriosus",
"Erimyzon sucetta",
"Fundulus chyrsotus",
"Fundulus confluentus",
"Gambusia holbrooki",
"Heterandria formosa",
"Hoplisternum littorale",
"Jordanella floridae",
"Lucania goodei",
"Lepomis marginatus",
"Lepomis punctatus",
"Palaemonetes paludosus",
"Planorbella spp.",
"Poecilia latipinna",
"Procambarus alleni",
"Procambarus fallax",
"Sirens lacertina"
) &
Slough == "SRS" & Season == "Dry" |
Species %in% c(
"Anomalochromis letourneuxi",
"Belostoma spp.",
"Celithemis spp.",
"Clarias batrachus",
"Coenagrionidae",
"Copepoda",
"Elassoma evergladei",
"Enneacanthus gloriosus",
"Erimyzon sucetta",
"Fundulus chrysotus",
"Gambusia holbrooki",
"Heterandria formosa",
"Jordanella floridae",
"Lepomis macrochirus",
"Lepomis marginatus",
"Lepomis microlophus",
"Lepomis punctatus",
"Libellula incesta",
"Lucania goodei",
"Macrognathus siamensis",
"Mayaheros urophthalmus",
"Oreochromis aureus",
"Palaemonetes paludosus",
"Pelocoris femoratus",
"Poecilia latipinna",
"Procambarus fallax"
) &
Slough == "SRS" & Season == "Wet" |
Species %in% c(
"Amphipoda",
"Chironomidae",
"Cladocera",
"Copepoda",
"Gambusia holbrooki",
"Heterandria formosa",
"Hydrachnidia",
"Lucania goodei",
"Ostracoda",
"Palaemonetes paludosus"
) &
Slough == "TSL" & Season == "Dry" |
Species %in% c(
"Amphipoda",
"Brachymesia gravida",
"Celithemis spp.",
"Chironomidae",
"Cichlasoma bimaculatum",
"Cichlasoma managuense",
"Coenagrionidae",
"Coleoptera",
"Copepoda",
"Enneacanthus gloriosus",
"Erythemis simplicicollis",
"Fundulus chrysotus",
"Gambusia holbrooki",
"Gerridae",
"Heterandria formosa",
"Hydrachnidia",
"Lepomis marginatus",
"Lucania goodei",
"Mayaheros urophthalmus",
"Monopterus albus/javensis",
"Notropis petersoni",
"Ostracoda",
"Palaemonetes paludosus",
"Pelocoris femoratus",
"Planorbella spp."
) &
Slough == "TSL" & Season == "Wet"
)
mutate(Species = fct_reorder(Species, TP_50),
Season = fct_rev(Season),
Habitat = fct_relevel(Habitat, c("Near Pond", "Marsh", "Pond"))),
aes(x = Species, y = TP_50, fill = Habitat, color = Habitat))+
facet_wrap(~Slough*Season, ncol = 1)+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
coord_cartesian(ylim = c(1.5, 6))+
ylab("Trophic Position")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.75),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(filename = "trophic_position_habitat_comparisons.tiff",
# plot = tp.habitat.comparisons.plot,
# width = 174, height = 120, units = "mm", dpi = 600)
4.3 Plot post-invasion alpha
tp.alpha.srs <- tp.alpha.plot %>%
filter(Slough == "SRS", Habitat != "NP") #%>%
#select(Habitat,Season, Species, TP_50, alpha_50) #%>%
#rename(`Trophic Position` = TP_50, Alpha = alpha_50) %>%
#pivot_longer(c("Trophic Position", "Alpha"), names_to = "Metric", values_to = "Median")
(tp.srs.plot <- ggplot(data = tp.alpha.plot %>%
filter(Slough == "SRS", Habitat != "Near Pond"),
aes(x = Species, y = TP_50,
color = Season, fill = Season))+
facet_wrap(~Habitat)+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5,
position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
ylab("Trophic Position")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(filename = "trophic_position_srs_post_isotopes.tiff",
# plot = tp.srs.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
(alpha.srs.plot <- ggplot(data = tp.alpha.plot %>%
filter(Slough == "SRS", Habitat != "Near Pond"),
aes(x = Species, y = alpha_50,
color = Season, fill = Season))+
facet_wrap(~Habitat)+
geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5,
position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
ylab("Alpha")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(filename = "alpha_srs_post_isotopes.tiff",
# plot = alpha.srs.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
#make the same plot but for alpha instead of trophic position
(alpha.current.plot <- ggplot(data = tp.alpha.plot, aes(x = Species, y = alpha_50, color = Season, fill = Season))+
facet_grid(Habitat ~ Slough)+
geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(expand = c(0,0))+
ylab("Alpha")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(filename = "alpha_habitat_season_slough.tiff",
# plot = alpha.current.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
herp.tp.alpha <- tp.alpha.plot %>%
filter(Species %in% c("Acris gryllus - tadpole", "Nerodia floridana", "Notophthalmus viridescens",
"Rana grylio", "Rana grylio - tadpole", "Sirens lacertina"))
(herp.tp.plot <- ggplot(data = herp.tp.alpha, aes(x = Species, y = TP_50, color = Habitat, shape = Season))+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 1, position = position_dodge(0.3), width = 1)+
geom_point(size = 3, position = position_dodge(0.3))+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(expand = c(0,0))+
ylab("Trophic Position")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 10),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8),
strip.text.x = element_text(size = 10),
strip.text.y = element_text(size = 10),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 10), axis.text.x.bottom = element_text(size = 10, angle = 30, hjust = 0.5),
axis.text.y = element_text(size = 10), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 10), legend.text = element_text(size = 10))
)
(herp.alpha.plot <- ggplot(data = herp.tp.alpha, aes(x = Species, y = alpha_50, color = Habitat, shape = Season))+
geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 1, position = position_dodge(0.3), width = 1)+
geom_point(size = 3, position = position_dodge(0.3))+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(expand = c(0,0))+
ylab("Alpha")+
xlab(NULL)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8),
strip.text.x = element_text(size = 10),
strip.text.y = element_text(size = 10),
#panel.border = element_blank(), panel.grid.major = element_blank(),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 10), axis.text.x.bottom = element_text(size = 10, angle = 30, hjust = 0.5),
axis.text.y = element_text(size = 10), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 10), legend.text = element_text(size = 10))
)
#library(showtext)
#showtext::showtext_auto()
#showtext::showtext_opts(dpi = 600)
windowsFonts(Times = windowsFont("Times New Roman"))
herp.tp.alpha.plot <- ggarrange(herp.tp.plot, herp.alpha.plot, ncol = 1, nrow = 2, common.legend = T,
labels = "AUTO", legend = "right", font.label = list(family = "Times"),
hjust = -0.2)
herp.tp.alpha.plot
# ggsave("herp_tp_alpha_plot.tiff",
# plot = herp.tp.alpha.plot,
# dpi = 600, height = 150, width = 200, units = "mm")4.4 Format output into tables
4.4.1 Trophic position tables
4.4.1.1 Seasonal comparisons
4.4.1.1.1 SRS marsh wet vs dry
srs.marsh.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.tp to bind w/ pairwise.tp probs
srs.marsh.season.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.marsh.season.direct.pairwise.tp <- srs.marsh.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.marsh.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.marsh.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.2 SRS near pond wet vs dry
srs.np.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.tp to bind w/ pairwise.tp probs
srs.np.season.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.np.season.direct.pairwise.tp <- srs.np.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.np.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.np.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.3 SRS Pond wet vs dry
srs.pond.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
srs.pond.season.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.pond.season.direct.pairwise.tp <- srs.pond.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.pond.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.pond.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.4 TSL marsh wet vs dry
tsl.marsh.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.marsh.season.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.marsh.season.direct.pairwise.tp <- tsl.marsh.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.marsh.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.marsh.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.5 TSL near pond wet vs dry
tsl.np.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.tp to bind w/ pairwise.tp probs
tsl.np.season.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.np.season.direct.pairwise.tp <- tsl.np.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.np.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.np.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.6 TSL pond wet vs dry
tsl.pond.season.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.pond.season.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.pond.season.direct.pairwise.tp <- tsl.pond.season.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.pond.season.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.pond.season.modes.tp) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.1.7 Combine seasonal comparisons
seasonal.pairwise.tp <- bind_rows(srs.marsh.season.direct.pairwise.tp, srs.np.season.direct.pairwise.tp, srs.pond.season.direct.pairwise.tp,
tsl.marsh.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax"
))
write.xlsx(seasonal.pairwise.tp, "seasonal_pairwise.tp.xlsx")
#proportion of changes that are statistically different
sum(seasonal.pairwise.tp$`Wet < Dry`>=0.95|seasonal.pairwise.tp$`Wet < Dry`<=0.05)/nrow(seasonal.pairwise.tp)
seasonal.pairwise.tp %>%
filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>%
mutate(deltaTP = abs(Wet - Dry)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
#break it down by habitat
#marsh
#frequency
sum(srs.marsh.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
srs.marsh.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
tsl.marsh.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
tsl.marsh.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
nrow(bind_rows(srs.marsh.season.direct.pairwise.tp, tsl.marsh.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.marsh.season.direct.pairwise.tp, tsl.marsh.season.direct.pairwise.tp) %>%
filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>%
mutate(deltaTP = abs(Wet - Dry)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
#np
#frequency
sum(srs.np.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
srs.np.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
tsl.np.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
tsl.np.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
nrow(bind_rows(srs.np.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.np.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp) %>%
filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>%
mutate(deltaTP = abs(Wet - Dry)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
#pond
#frequency
sum(srs.pond.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
srs.pond.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
tsl.pond.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
tsl.pond.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
nrow(bind_rows(srs.pond.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.pond.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp) %>%
filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>%
mutate(deltaTP = abs(Wet - Dry)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))4.4.1.2 Slough Comparisons
4.4.1.2.1 Marsh wet season
slough.marsh.wet.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.marsh.wet.modes.tp <- trophic.positions %>%
filter(Season == "Wet", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.marsh.wet.direct.pairwise.tp <- slough.marsh.wet.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.wet.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.marsh.wet.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.2 Near pond wet season
slough.np.wet.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.np.wet.modes.tp <- trophic.positions %>%
filter(Season == "Wet", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.np.wet.direct.pairwise.tp <- slough.np.wet.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.np.wet.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.np.wet.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.3 Pond wet season
slough.pond.wet.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.pond.wet.modes.tp <- trophic.positions %>%
filter(Season == "Wet", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.pond.wet.direct.pairwise.tp <- slough.pond.wet.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.pond.wet.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.pond.wet.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.4 Marsh dry season
slough.marsh.dry.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.marsh.dry.modes.tp <- trophic.positions %>%
filter(Season == "Dry", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.marsh.dry.direct.pairwise.tp <- slough.marsh.dry.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.dry.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.marsh.dry.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.5 Near pond dry season
slough.np.dry.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.np.dry.modes.tp <- trophic.positions %>%
filter(Season == "Dry", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.np.dry.direct.pairwise.tp <- slough.np.dry.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.np.dry.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.np.dry.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.6 Pond dry season
slough.pond.dry.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
slough.pond.dry.modes.tp <- trophic.positions %>%
filter(Season == "Dry", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.pond.dry.direct.pairwise.tp <- slough.pond.dry.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.pond.dry.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.pond.dry.modes.tp) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.2.7 Combine slough comparisons
slough.pairwise.tp <- bind_rows(slough.marsh.wet.direct.pairwise.tp, slough.np.wet.direct.pairwise.tp, slough.pond.wet.direct.pairwise.tp,
slough.marsh.dry.direct.pairwise.tp, slough.np.dry.direct.pairwise.tp, slough.pond.dry.direct.pairwise.tp) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "TILMAR" ~ "T. mariae"
))
write.csv(slough.pairwise.tp,
"Outputs/Stable Isotopes/Trophic Position/Post-Invasion/slough_pairwise.tp.csv",
row.names = F)
#proportion of changes that are statistically different
sum(slough.pairwise.tp$`SRS < TSL`>=0.95|slough.pairwise.tp$`SRS < TSL`<=0.05)/nrow(slough.pairwise.tp)
slough.pairwise %>%
filter(`SRS < TSL` >= 0.95 | `SRS < TSL` <= 0.05) %>%
mutate(deltaTP = abs(SRS - TSL)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))4.4.1.3 Habitat comparisons
4.4.1.3.1 SRS wet marsh vs near pond
srs.wet.marsh.np.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
srs.wet.marsh.np.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
srs.wet.marsh.np.direct.pairwise.tp <- srs.wet.marsh.np.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.np.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., srs.wet.marsh.np.modes.tp) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.2 SRS wet marsh vs pond
srs.wet.marsh.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.tp to bind w/ pairwise.tp probs
srs.wet.marsh.pond.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
srs.wet.marsh.pond.direct.pairwise.tp <- srs.wet.marsh.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., srs.wet.marsh.pond.modes.tp) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.3 SRS wet near pond vs pond
srs.wet.np.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.tp to bind w/ pairwise.tp probs
srs.wet.np.pond.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
srs.wet.np.pond.direct.pairwise.tp <- srs.wet.np.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.np.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., srs.wet.np.pond.modes.tp) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.4 TSL wet marsh vs near pond
tsl.wet.marsh.np.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.marsh.np.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
tsl.wet.marsh.np.direct.pairwise.tp <- tsl.wet.marsh.np.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.np.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., tsl.wet.marsh.np.modes.tp) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.5 TSL wet marsh vs pond
tsl.wet.marsh.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.marsh.pond.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
tsl.wet.marsh.pond.direct.pairwise.tp <- tsl.wet.marsh.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., tsl.wet.marsh.pond.modes.tp) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.6 TSL wet near pond vs pond
tsl.wet.np.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.np.pond.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
tsl.wet.np.pond.direct.pairwise.tp <- tsl.wet.np.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.np.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., tsl.wet.np.pond.modes.tp) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.7 SRS dry marsh vs near pond
srs.dry.marsh.np.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
srs.dry.marsh.np.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
srs.dry.marsh.np.direct.pairwise.tp <- srs.dry.marsh.np.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.np.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., srs.dry.marsh.np.modes.tp) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.8 SRS dry marsh vs pond
srs.dry.marsh.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
srs.dry.marsh.pond.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
srs.dry.marsh.pond.direct.pairwise.tp <- srs.dry.marsh.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., srs.dry.marsh.pond.modes.tp) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.9 SRS dry near pond vs pond
srs.dry.np.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
srs.dry.np.pond.modes.tp <- trophic.positions %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
srs.dry.np.pond.direct.pairwise.tp <- srs.dry.np.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.np.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., srs.dry.np.pond.modes.tp) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.10 TSL dry marsh vs near pond
tsl.dry.marsh.np.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.marsh.np.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
tsl.dry.marsh.np.direct.pairwise.tp <- tsl.dry.marsh.np.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.np.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., tsl.dry.marsh.np.modes.tp) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.11 TSL dry marsh vs pond
tsl.dry.marsh.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.marsh.pond.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
tsl.dry.marsh.pond.direct.pairwise.tp <- tsl.dry.marsh.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., tsl.dry.marsh.pond.modes.tp) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.12 TSL dry near pond vs pond
tsl.dry.np.pond.pairwise.tp <- pairwiseTP %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.np.pond.modes.tp <- trophic.positions %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
tsl.dry.np.pond.direct.pairwise.tp <- tsl.dry.np.pond.pairwise.tp %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.np.pond.pairwise.tp)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., tsl.dry.np.pond.modes.tp) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.1.3.13 Combine habitat comparisons
marsh.np.pairwise.tp <- bind_rows(srs.wet.marsh.np.direct.pairwise.tp, srs.dry.marsh.np.direct.pairwise.tp,
tsl.wet.marsh.np.direct.pairwise.tp, tsl.dry.marsh.np.direct.pairwise.tp) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for marsh-np
sum(marsh.np.pairwise.tp$`Marsh < NP`>=0.95|marsh.np.pairwise.tp$`Marsh < NP`<=0.05)/nrow(marsh.np.pairwise.tp)
marsh.np.pairwise.tp %>%
filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>%
mutate(deltaTP = abs(Marsh - NP)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
marsh.pond.pairwise.tp <- bind_rows(srs.wet.marsh.pond.direct.pairwise.tp, srs.dry.marsh.pond.direct.pairwise.tp,
tsl.wet.marsh.pond.direct.pairwise.tp, tsl.dry.marsh.pond.direct.pairwise.tp) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLABAT" ~ "C. batrachus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "HOPLIT" ~ "H. littorale",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LIBINC" ~ "L. incesta",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OREAUR" ~ "O. aureus",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "SIRLAC" ~ "S. lacertina",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for marsh-pond
sum(marsh.pond.pairwise.tp$`Marsh < Pond`>=0.95|marsh.pond.pairwise.tp$`Marsh < Pond`<=0.05)/nrow(marsh.pond.pairwise.tp)
marsh.pond.pairwise.tp %>%
filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>%
mutate(deltaTP = abs(Marsh - Pond)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
np.pond.pairwise.tp <- bind_rows(srs.wet.np.pond.direct.pairwise.tp, srs.dry.np.pond.direct.pairwise.tp,
tsl.wet.np.pond.direct.pairwise.tp, tsl.dry.np.pond.direct.pairwise.tp) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for np-pond
sum(np.pond.pairwise.tp$`NP < Pond`>=0.95|np.pond.pairwise.tp$`NP < Pond`<=0.05)/nrow(np.pond.pairwise.tp)
np.pond.pairwise.tp %>%
filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>%
mutate(deltaTP = abs(NP - Pond)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
#proportion of all habitat changes
(sum(marsh.np.pairwise.tp$`Marsh < NP`>=0.95|marsh.np.pairwise.tp$`Marsh < NP`<=0.05) +
sum(marsh.pond.pairwise.tp$`Marsh < Pond`>=0.95|marsh.pond.pairwise.tp$`Marsh < Pond`<=0.05)+
sum(np.pond.pairwise.tp$`NP < Pond`>=0.95|np.pond.pairwise.tp$`NP < Pond`<=0.05))/(
nrow(marsh.np.pairwise.tp)+nrow(marsh.pond.pairwise.tp)+nrow(np.pond.pairwise.tp)
)
bind_rows(
marsh.np.pairwise.tp %>% rename(Comparison = `Marsh < NP`, A = Marsh, B = NP),
marsh.pond.pairwise.tp %>% rename(Comparison = `Marsh < Pond`, A = Marsh, B = Pond),
np.pond.pairwise.tp %>% rename(Comparison = `NP < Pond`, A = NP, B = Pond)
) %>%
filter(Comparison >= 0.95 | Comparison <= 0.05) %>%
mutate(deltaTP = abs(A - B)) %>%
summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
habitat.pairwise.tp <- list("Marsh-NP" = marsh.np.pairwise.tp,
"Marsh-Pond" = marsh.pond.pairwise.tp,
"NP-Pond" = np.pond.pairwise.tp)
#export to Excel (has multiple tabs)
write.xlsx(habitat.pairwise.tp,
"Outputs/Stable Isotopes/Trophic Position/Post-Invasion/habitat_pairwise_tp.xlsx")4.4.2 Alpha tables
4.4.2.1 Seasonal comparisons
4.4.2.1.1 SRS marsh wet vs dry
srs.marsh.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.alpha to bind w/ pairwise.alpha probs
srs.marsh.season.modes.alpha <- alpha %>%
filter(Slough == "SRS", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.marsh.season.direct.pairwise.alpha <- srs.marsh.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.marsh.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.marsh.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.2 SRS near pond wet vs dry
srs.np.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.np.season.modes.alpha <- alpha %>%
filter(Slough == "SRS", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.np.season.direct.pairwise.alpha <- srs.np.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.np.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.np.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.3 SRS pond wet vs dry
srs.pond.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.pond.season.modes.alpha <- alpha %>%
filter(Slough == "SRS", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
srs.pond.season.direct.pairwise.alpha <- srs.pond.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.pond.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., srs.pond.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.4 TSL marsh wet vs dry
tsl.marsh.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.marsh.season.modes.alpha <- alpha %>%
filter(Slough == "TSL", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.marsh.season.direct.pairwise.alpha <- tsl.marsh.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.marsh.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.marsh.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.5 TSL near pond wet vs dry
tsl.np.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.alpha to bind w/ pairwise.alpha probs
tsl.np.season.modes.alpha <- alpha %>%
filter(Slough == "TSL", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.np.season.direct.pairwise.alpha <- tsl.np.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.np.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.np.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.6 TSL pond wet vs dry
tsl.pond.season.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.pond.season.modes.alpha <- alpha %>%
filter(Slough == "TSL", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>%
filter(!is.na(Dry), !is.na(Wet))
#combine these
tsl.pond.season.direct.pairwise.alpha <- tsl.pond.season.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.pond.season.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Wet < Dry` = ".") %>%
merge(., tsl.pond.season.modes.alpha) %>%
select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>%
mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.1.7 Combine seasonal comparisons
seasonal.pairwise.alpha <- bind_rows(srs.marsh.season.direct.pairwise.alpha, srs.np.season.direct.pairwise.alpha, srs.pond.season.direct.pairwise.alpha,
tsl.marsh.season.direct.pairwise.alpha, tsl.np.season.direct.pairwise.alpha, tsl.pond.season.direct.pairwise.alpha) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax"
))
write.csv(seasonal.pairwise.alpha,
"Outputs/Stable Isotopes/Trophic Position/Post-Invasion/seasonal_pairwise.alpha.csv",
row.names = F)
#proportion of changes that are statistically different
sum(seasonal.pairwise.alpha$`Wet < Dry`>=0.95|seasonal.pairwise.alpha$`Wet < Dry`<=0.05)/nrow(seasonal.pairwise.alpha)4.4.2.2 Slough comparisons
4.4.2.2.1 Marsh wet season
slough.marsh.wet.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
slough.marsh.wet.modes.alpha <- alpha %>%
filter(Season == "Wet", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.marsh.wet.direct.pairwise.alpha <- slough.marsh.wet.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.wet.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.marsh.wet.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.2.2 Near pond wet season
slough.np.wet.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
slough.np.wet.modes.alpha <- alpha %>%
filter(Season == "Wet", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.np.wet.direct.pairwise.alpha <- slough.np.wet.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.np.wet.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.np.wet.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.2.3 Pond wet season
slough.pond.wet.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
slough.pond.wet.modes.alpha <- alpha %>%
filter(Season == "Wet", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.pond.wet.direct.pairwise.alpha <- slough.pond.wet.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.pond.wet.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.pond.wet.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))
4.4.2.2.4 Marsh dry season
slough.marsh.dry.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.alpha to bind w/ pairwise.alpha probs
slough.marsh.dry.modes.alpha <- alpha %>%
filter(Season == "Dry", Habitat == "Marsh") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.marsh.dry.direct.pairwise.alpha <- slough.marsh.dry.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.dry.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.marsh.dry.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.2.5 Near pond dry season
slough.np.dry.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
slough.np.dry.modes.alpha <- alpha %>%
filter(Season == "Dry", Habitat == "NP") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.np.dry.direct.pairwise.alpha <- slough.np.dry.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.np.dry.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.np.dry.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.2.6 Pond dry season
slough.pond.dry.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
slough.pond.dry.modes.alpha <- alpha %>%
filter(Season == "Dry", Habitat == "Pond") %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>%
filter(!is.na(TSL), !is.na(SRS))
#combine these
slough.pond.dry.direct.pairwise.alpha <- slough.pond.dry.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(slough.pond.dry.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`SRS < TSL` = ".") %>%
merge(., slough.pond.dry.modes.alpha) %>%
select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>%
mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.2.7 Combine slough comparisons
slough.pairwise.alpha <- bind_rows(slough.marsh.wet.direct.pairwise.alpha, slough.np.wet.direct.pairwise.alpha, slough.pond.wet.direct.pairwise.alpha,
slough.marsh.dry.direct.pairwise.alpha, slough.np.dry.direct.pairwise.alpha, slough.pond.dry.direct.pairwise.alpha) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "TILMAR" ~ "T. mariae"
))
write.csv(slough.pairwise.alpha,
"Outputs/Stable Isotopes/Trophic Position/Post-Invasion/slough_pairwise.alpha.csv",
row.names = F)
#proportion of changes that are statistically different
sum(slough.pairwise.alpha$`SRS < TSL`>=0.95|slough.pairwise.alpha$`SRS < TSL`<=0.05)/nrow(slough.pairwise.alpha)4.4.2.3 Habitat comparisons
4.4.2.3.1 SRS wet marsh vs near pond
srs.wet.marsh.np.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.marsh.np.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
srs.wet.marsh.np.direct.pairwise.alpha <- srs.wet.marsh.np.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.np.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., srs.wet.marsh.np.modes.alpha) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.2 SRS wet marsh vs pond
srs.wet.marsh.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.marsh.pond.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
srs.wet.marsh.pond.direct.pairwise.alpha <- srs.wet.marsh.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., srs.wet.marsh.pond.modes.alpha) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.3 SRS wet near pond vs pond
srs.wet.np.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.np.pond.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Wet", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
srs.wet.np.pond.direct.pairwise.alpha <- srs.wet.np.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.wet.np.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., srs.wet.np.pond.modes.alpha) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.4 TSL wet marsh vs near pond
tsl.wet.marsh.np.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.marsh.np.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
tsl.wet.marsh.np.direct.pairwise.alpha <- tsl.wet.marsh.np.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.np.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., tsl.wet.marsh.np.modes.alpha) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.5 TSL wet marsh vs pond
tsl.wet.marsh.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.marsh.pond.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
tsl.wet.marsh.pond.direct.pairwise.alpha <- tsl.wet.marsh.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., tsl.wet.marsh.pond.modes.alpha) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.6 TSL wet near pond vs pond
tsl.wet.np.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.np.pond.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Wet", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
tsl.wet.np.pond.direct.pairwise.alpha <- tsl.wet.np.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.np.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., tsl.wet.np.pond.modes.alpha) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.7 SRS dry marsh vs near pond
srs.dry.marsh.np.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].Warning: Expected 4 pieces. Additional pieces discarded in 279 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.marsh.np.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
srs.dry.marsh.np.direct.pairwise.alpha <- srs.dry.marsh.np.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.np.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., srs.dry.marsh.np.modes.alpha) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.8 SRS dry marsh vs pond
srs.dry.marsh.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.marsh.pond.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
srs.dry.marsh.pond.direct.pairwise.alpha <- srs.dry.marsh.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., srs.dry.marsh.pond.modes.alpha) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.9 SRS dry near pond vs pond
srs.dry.np.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.np.pond.modes.alpha <- alpha %>%
filter(Slough == "SRS", Season == "Dry", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
srs.dry.np.pond.direct.pairwise.alpha <- srs.dry.np.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(srs.dry.np.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., srs.dry.np.pond.modes.alpha) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.10 TSL dry marsh vs near pond
tsl.dry.marsh.np.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.marsh.np.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(NP))
#combine these
tsl.dry.marsh.np.direct.pairwise.alpha <- tsl.dry.marsh.np.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.np.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < NP` = ".") %>%
merge(., tsl.dry.marsh.np.modes.alpha) %>%
select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>%
mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.11 TSL dry marsh vs pond
tsl.dry.marsh.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.marsh.pond.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(Marsh), !is.na(Pond))
#combine these
tsl.dry.marsh.pond.direct.pairwise.alpha <- tsl.dry.marsh.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`Marsh < Pond` = ".") %>%
merge(., tsl.dry.marsh.pond.modes.alpha) %>%
select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.12 TSL dry near pond vs pond
tsl.dry.np.pond.pairwise.alpha <- pairwisealpha %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>%
select(-c(Slough, Habitat, Season)) %>%
column_to_rownames(var = "Species") %>%
t() %>% as.data.frame() %>%
rownames_to_column(var = "Species") %>%
mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>%
separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>%
filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>%
filter(Species %in% colnames(.)) %>%
select(Slough, Habitat, Season, Species, matches(.$Species)) %>%
unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
column_to_rownames(var = "Slough_Habitat_Season_Species")
#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.np.pond.modes.alpha <- alpha %>%
filter(Slough == "TSL", Season == "Dry", Habitat %in% c("NP", "Pond")) %>%
rename(Mode = `50%`) %>%
pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>%
filter(!is.na(NP), !is.na(Pond))
#combine these
tsl.dry.np.pond.direct.pairwise.alpha <- tsl.dry.np.pond.pairwise.alpha %>% as.matrix %>%
diag() %>% as.data.frame() %>%
mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.np.pond.pairwise.alpha)) %>%
separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>%
rename(`NP < Pond` = ".") %>%
merge(., tsl.dry.np.pond.modes.alpha) %>%
select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>%
mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>%
mutate(across(where(is.numeric), round, 2))4.4.2.3.13 Combine habitat comparisons
marsh.np.pairwise.alpha <- bind_rows(srs.wet.marsh.np.direct.pairwise.alpha, srs.dry.marsh.np.direct.pairwise.alpha,
tsl.wet.marsh.np.direct.pairwise.alpha, tsl.dry.marsh.np.direct.pairwise.alpha) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for marsh-np
sum(marsh.np.pairwise.alpha$`Marsh < NP`>=0.95|marsh.np.pairwise.alpha$`Marsh < NP`<=0.05)/nrow(marsh.np.pairwise.alpha)
marsh.pond.pairwise.alpha <- bind_rows(srs.wet.marsh.pond.direct.pairwise.alpha, srs.dry.marsh.pond.direct.pairwise.alpha,
tsl.wet.marsh.pond.direct.pairwise.alpha, tsl.dry.marsh.pond.direct.pairwise.alpha) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLABAT" ~ "C. batrachus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "HOPLIT" ~ "H. littorale",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LIBINC" ~ "L. incesta",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OREAUR" ~ "O. aureus",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "SIRLAC" ~ "S. lacertina",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for marsh-pond
sum(marsh.pond.pairwise.alpha$`Marsh < Pond`>=0.95|marsh.pond.pairwise.alpha$`Marsh < Pond`<=0.05)/nrow(marsh.pond.pairwise.alpha)
np.pond.pairwise.alpha <- bind_rows(srs.wet.np.pond.direct.pairwise.alpha, srs.dry.np.pond.direct.pairwise.alpha,
tsl.wet.np.pond.direct.pairwise.alpha, tsl.dry.np.pond.direct.pairwise.alpha) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "AMPHIPOD" ~ "Amphipoda",
Species == "BELBEL" ~ "B. belizanus",
Species == "BELSPP" ~ "Belostoma spp",
Species == "BLUE" ~ "Hydrachnidia",
Species == "BRAGRA" ~ "B. gravida",
Species == "CELSPP" ~ "Celithemus spp.",
Species == "CHIRON" ~ "Chironomidae",
Species == "CICBIM" ~ "C. bimaculatum",
Species == "CICMAN" ~ "C. managuense",
Species == "CICURO" ~ "C. urophthalmus",
Species == "CLADOCERA" ~ "Cladocera",
Species == "COENAG" ~ "Coenagrionidae",
Species == "COLEOA" ~ "Coleoptera",
Species == "COPEPOD" ~ "Copepoda",
Species == "CORIXI" ~ "Corixidae",
Species == "ELAEVE" ~ "E. evergladei",
Species == "ENNGLO" ~ "E. gloriosus",
Species == "EPHEME" ~ "Ephemeroptera",
Species == "ERISUC" ~ "E. sucetta",
Species == "ERYSIM" ~ "E. simplicicollis",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "FUNCON" ~ "F. confluentus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "GERRID" ~ "Gerridae",
Species == "HEMLET" ~ "H. letourneuxi",
Species == "HETFOR" ~ "H. formosa",
Species == "JORFLO" ~ "J. floridae",
Species == "LEPGUL" ~ "L. gulosus",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMAR" ~ "L. marginatus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPLA" ~ "L. platyrhincus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "LUCGOO" ~ "L. goodei",
Species == "MACSIA" ~ "M. siamensis",
Species == "MICSAL" ~ "M. salmoides",
Species == "MONALB" ~ "M. albus",
Species == "NOTPET" ~ "N. petersoni",
Species == "OSTRACOD" ~ "Ostracoda",
Species == "PALPAL" ~ "P. paludosus",
Species == "PELFEM" ~ "P. femoratus",
Species == "PERMAT" ~ "Periphyton (Mat)",
Species == "PLASCA" ~ "Planorbella spp.",
Species == "POELAT" ~ "P. latipinna",
Species == "PROALL" ~ "P. alleni",
Species == "PROFAL" ~ "P. fallax",
Species == "PROSPP" ~ "Procambarus spp.",
Species == "TILMAR" ~ "T. mariae"))
#proportion of statistically different comparisons for np-pond
sum(np.pond.pairwise.alpha$`NP < Pond`>=0.95|np.pond.pairwise.alpha$`NP < Pond`<=0.05)/nrow(np.pond.pairwise.alpha)
#proportion of changes across all habitat comparisons
(
sum(marsh.np.pairwise.alpha$`Marsh < NP`>=0.95|marsh.np.pairwise.alpha$`Marsh < NP`<=0.05)+
sum(marsh.pond.pairwise.alpha$`Marsh < Pond`>=0.95|marsh.pond.pairwise.alpha$`Marsh < Pond`<=0.05)+
sum(np.pond.pairwise.alpha$`NP < Pond`>=0.95|np.pond.pairwise.alpha$`NP < Pond`<=0.05)
)/(
nrow(marsh.np.pairwise.alpha)+nrow(marsh.pond.pairwise.alpha)+nrow(np.pond.pairwise.alpha)
)
habitat.pairwise.alpha <- list("Marsh-NP" = marsh.np.pairwise.alpha,
"Marsh-Pond" = marsh.pond.pairwise.alpha,
"NP-Pond" = np.pond.pairwise.alpha)
#export to Excel
write.xlsx(habitat.pairwise.alpha,
"Outputs/Stable Isotopes/Trophic Position/Post-Invasion/habitat_pairwise_alpha.xlsx")5 Pre- vs Post-Invasion tRophic Position Models
pre.post.list <- extractIsotopeData(pre.post.invasion, b1 = "b1", b2 = "b2", baselineColumn = "Sample_Type",
consumersColumn = "Species", groupsColumn = "Community",
d13C = "d13C", d15N = "d15N")
for (community in pre.post.list) {
print(summary(community))
plot(community)
}
Summary for stable isotope data object
Group Pre-Invasion
Consumer AMENAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.6 0.8 0.3 8.5 10.3 9.8
dCc 5 -26.1 0.6 0.3 -26.8 -25.5 -25.8
Summary for stable isotope data object
Group Pre-Invasion
Consumer ANISOP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.6 0.6 0.3 4.8 6.2 5.6
dCc 5 -31.1 0.8 0.4 -32.2 -30.1 -31.1
Summary for stable isotope data object
Group Pre-Invasion
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.5 1 0.4 4 6.4 5.6
dCc 5 -29.4 1.5 0.7 -31.2 -27 -29.7
Summary for stable isotope data object
Group Pre-Invasion
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.4 0.6 0.3 7.6 9 8.5
dCc 5 -27 1 0.4 -28.2 -25.9 -26.8
Summary for stable isotope data object
Group Pre-Invasion
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 7.7 0.6 0.3 7 8.6 7.6
dCc 5 -27 0.7 0.3 -28 -26.1 -27.1
Summary for stable isotope data object
Group Pre-Invasion
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.9 0.5 0.2 9.2 10.5 9.7
dCc 5 -27.4 0.4 0.2 -28 -27.1 -27.3
Summary for stable isotope data object
Group Pre-Invasion
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 7 9.8 0.6 0.2 9.2 10.5 9.7
dCc 7 -26.5 1.1 0.4 -27.3 -24.2 -26.9
Summary for stable isotope data object
Group Pre-Invasion
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 10.6 0.3 0.1 10.3 11 10.5
dCc 5 -28.5 0.1 0.1 -28.7 -28.4 -28.4
Summary for stable isotope data object
Group Pre-Invasion
Consumer LEPMAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.3 0.6 0.3 8.5 10 9.6
dCc 5 -24.4 4.9 2.2 -28.7 -15.9 -26
Summary for stable isotope data object
Group Pre-Invasion
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.7 1 0.5 7.3 9.8 9
dCc 5 -28 0.4 0.2 -28.6 -27.5 -28
Summary for stable isotope data object
Group Pre-Invasion
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 9.9 0.4 0.2 9.4 10.3 10.1
dCc 5 -26.7 0.5 0.2 -27.5 -26.3 -26.5
Summary for stable isotope data object
Group Pre-Invasion
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.2 0.1 0.1 8 8.4 8.2
dCc 5 -28.6 0.2 0.1 -28.9 -28.3 -28.6
Summary for stable isotope data object
Group Pre-Invasion
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 8.3 1.3 0.6 7.1 9.9 8
dCc 5 -29.4 0.4 0.2 -29.8 -29 -29.6
Summary for stable isotope data object
Group Pre-Invasion
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 20 2.6 0.8 0.2 0.7 3.9 2.7
dCb1 20 -29.1 1.9 0.4 -32.4 -25.6 -29.2
dNb2 20 3.9 0.4 0.1 3.3 4.7 3.8
dCb2 20 -31.6 1.2 0.3 -33.5 -28.9 -31.8
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 5.4 0.6 0.3 4.5 6 5.4
dCc 5 -27.4 0.4 0.2 -28.1 -27.1 -27.4
Summary for stable isotope data object
Group Post-Invasion
Consumer AMENAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 11.1 1.2 0.6 9.4 12.5 11.3
dCc 4 -30.6 0.6 0.3 -31.3 -29.9 -30.6
Summary for stable isotope data object
Group Post-Invasion
Consumer BELSPP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 6 5 1.2 0.5 3.7 7.2 4.8
dCc 6 -31.1 0.7 0.3 -32.1 -30.3 -31
Summary for stable isotope data object
Group Post-Invasion
Consumer ANISOP
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.7 0.2 0.1 4.5 5 4.7
dCc 5 -32.1 0.2 0.1 -32.4 -32 -32.1
Summary for stable isotope data object
Group Post-Invasion
Consumer CICURO
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 16 8.5 0.7 0.2 7 9.4 8.6
dCc 16 -29.9 2.3 0.6 -32.7 -26.9 -31.3
Summary for stable isotope data object
Group Post-Invasion
Consumer ERISUC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 4 8.6 1.3 0.7 6.6 9.4 9.3
dCc 4 -31.3 1 0.5 -31.9 -29.8 -31.7
Summary for stable isotope data object
Group Post-Invasion
Consumer FUNCHR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 28 9 0.8 0.1 7.4 10 9.2
dCc 28 -28.5 1.4 0.3 -30.6 -25.8 -28.4
Summary for stable isotope data object
Group Post-Invasion
Consumer GAMHOL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 60 8.7 0.6 0.1 7.6 10.1 8.7
dCc 60 -30.2 0.7 0.1 -31.9 -28.9 -30.2
Summary for stable isotope data object
Group Post-Invasion
Consumer HETFOR
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 14 9.2 0.5 0.1 7.9 9.8 9.3
dCc 14 -31 1.1 0.3 -32.7 -28.9 -31.2
Summary for stable isotope data object
Group Post-Invasion
Consumer LEPMAC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 19 10 0.9 0.2 8.6 11.4 9.9
dCc 19 -29.8 0.9 0.2 -31.1 -27.9 -29.8
Summary for stable isotope data object
Group Post-Invasion
Consumer LEPMIC
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 8 9.6 0.8 0.3 8.3 10.7 9.5
dCc 8 -31.6 2.2 0.8 -33.8 -27.8 -32.3
Summary for stable isotope data object
Group Post-Invasion
Consumer LEPPUN
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 10.2 0.8 0.4 8.8 10.9 10.6
dCc 5 -30.6 1.1 0.5 -31.6 -28.9 -31.1
Summary for stable isotope data object
Group Post-Invasion
Consumer PALPAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 11 7.3 0.9 0.3 6.2 8.8 6.9
dCc 11 -31.3 0.6 0.2 -31.9 -29.8 -31.3
Summary for stable isotope data object
Group Post-Invasion
Consumer POELAT
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 9 8.7 0.2 0.1 8.3 9 8.7
dCc 9 -32.9 0.9 0.3 -34.5 -31.6 -32.4
Summary for stable isotope data object
Group Post-Invasion
Consumer PROFAL
Baseline 1 b1
Baseline 2 b2
n mean sd se min max median
dNb1 4 2.6 0.4 0.2 2 2.9 2.8
dCb1 4 -28.9 0.6 0.3 -29.5 -28.2 -28.9
dNb2 20 4.1 0.4 0.1 3.4 4.7 4.1
dCb2 20 -31.9 1.2 0.3 -34.7 -29.5 -31.7
deltaN 56 3.4 1 0.1 1.1 5.4 3.3
deltaC 107 0.4 1.3 0.1 -3.2 3 0.6
dNc 5 4.7 1 0.4 3.2 5.8 4.8
dCc 5 -29.6 0.5 0.2 -30.1 -28.8 -29.7 
pre.post.models <- multiSpeciesTP(pre.post.list, model = "twoBaselinesFull",
n.adapt = 10000, n.iter = 10000,
burnin = 1000, thin = 10, chains = 2, print = F)Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
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|+++ | 6%
|
|++++ | 8%
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
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|
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|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
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|** | 4%
|
|*** | 6%
|
|**** | 8%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 257
Unobserved stochastic nodes: 30
Total graph size: 312
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
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|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
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|
|*********** | 22%
|
|************ | 24%
|
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|
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|
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|**************** | 32%
|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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|
|********************************************** | 92%
|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
|+++++++++++++ | 26%
|
|++++++++++++++ | 28%
|
|+++++++++++++++ | 30%
|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
|++++++++++++++++++ | 36%
|
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|
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|
|+++++++++++++++++++++ | 42%
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|
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|
|++++++++++++++++++++++++ | 48%
|
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|
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|
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|
|++++++++++++++++++++++++++++ | 56%
|
|+++++++++++++++++++++++++++++ | 58%
|
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|
|+++++++++++++++++++++++++++++++ | 62%
|
|++++++++++++++++++++++++++++++++ | 64%
|
|+++++++++++++++++++++++++++++++++ | 66%
|
|++++++++++++++++++++++++++++++++++ | 68%
|
|+++++++++++++++++++++++++++++++++++ | 70%
|
|++++++++++++++++++++++++++++++++++++ | 72%
|
|+++++++++++++++++++++++++++++++++++++ | 74%
|
|+++++++++++++++++++++++++++++++++++++++ | 78%
|
|++++++++++++++++++++++++++++++++++++++++ | 80%
|
|+++++++++++++++++++++++++++++++++++++++++ | 82%
|
|++++++++++++++++++++++++++++++++++++++++++ | 84%
|
|+++++++++++++++++++++++++++++++++++++++++++ | 86%
|
|++++++++++++++++++++++++++++++++++++++++++++ | 88%
|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
|
|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
|
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
|
|**** | 8%
|
|***** | 10%
|
|****** | 12%
|
|******* | 14%
|
|******** | 16%
|
|********* | 18%
|
|********** | 20%
|
|*********** | 22%
|
|************ | 24%
|
|************* | 26%
|
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|
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|
|**************** | 32%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|******************************** | 64%
|
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|
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|
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|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|+++++ | 10%
|
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|
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|
|************************************************ | 96%
|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|
|++++++++ | 16%
|
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|
| | 0%
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 253
Unobserved stochastic nodes: 26
Total graph size: 304
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|
|++++++++++ | 20%
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|
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|
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|++++++++++++++++++++++++++++++++++++++++++ | 84%
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|
|+++++++++++++++++++++++++++++++++++++++++++++ | 90%
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|++++++++++++++++++++++++++++++++++++++++++++++ | 92%
|
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|**** | 8%
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|***** | 10%
|
|****** | 12%
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|******************************** | 64%
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|
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|
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|
|*********************************************** | 94%
|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
|+++++++++ | 18%
|
|++++++++++ | 20%
|
|+++++++++++ | 22%
|
|++++++++++++ | 24%
|
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|
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|
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|
|++++++++++++++++ | 32%
|
|+++++++++++++++++ | 34%
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|+++++++++++++++++++++++++++++ | 58%
|
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|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 223
Unobserved stochastic nodes: 28
Total graph size: 276
Initializing model
|
| | 0%
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 243
Unobserved stochastic nodes: 48
Total graph size: 316
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 219
Unobserved stochastic nodes: 24
Total graph size: 268
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 267
Unobserved stochastic nodes: 72
Total graph size: 364
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
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|
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|++++++++ | 16%
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|
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
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|* | 2%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 331
Unobserved stochastic nodes: 136
Total graph size: 492
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
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|+++++++++ | 18%
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|++++++++++ | 20%
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|
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 94%
|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
|
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98%
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100%
|
| | 0%
|
|* | 2%
|
|** | 4%
|
|*** | 6%
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|**** | 8%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 239
Unobserved stochastic nodes: 44
Total graph size: 308
Initializing model
|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 249
Unobserved stochastic nodes: 54
Total graph size: 328
Initializing model
|
| | 0%
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|+ | 2%
|
|++ | 4%
|
|+++ | 6%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 227
Unobserved stochastic nodes: 32
Total graph size: 284
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
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|
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|
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Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
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|
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|
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|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 233
Unobserved stochastic nodes: 38
Total graph size: 296
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 229
Unobserved stochastic nodes: 34
Total graph size: 288
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
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|++++++++ | 16%
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|
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96%
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| | 0%
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|
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|
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|
|************************************************ | 96%
|
|************************************************* | 98%
|
|**************************************************| 100%
Compiling model graph
Resolving undeclared variables
Allocating nodes
Graph information:
Observed stochastic nodes: 221
Unobserved stochastic nodes: 26
Total graph size: 272
Initializing model
|
| | 0%
|
|+ | 2%
|
|++ | 4%
|
|+++ | 6%
|
|++++ | 8%
|
|+++++ | 10%
|
|++++++ | 12%
|
|+++++++ | 14%
|
|++++++++ | 16%
|
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|++++++++++ | 20%
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|
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|
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|**************************************************| 100%#plot communities
credibilityIntervals(pre.post.models$df, x = "group", xlab = "Community")
5.1 Extract model output
#trophic position
#extract modes and credible intervals
pre.post.trophic.positions <- sapply(pre.post.models$TPs, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>%
as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>%
separate(rowname, into = c("Time", "Invasion", "Species"), sep = "\\.") %>%
unite(col = "Invasion Status", c(Time, Invasion), sep = "-") %>%
arrange(Species)
#write out to csv
write.csv(pre.post.trophic.positions,
file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_trophic_position_mode_ci.csv",
row.names = F)
#pairwise comparisons of trophic position
pre.post.pairwiseTP <- pairwiseComparisons(pre.post.models$TPs) %>% as.data.frame() %>% rownames_to_column()
colnames(pre.post.pairwiseTP) <- c("Species", pre.post.pairwiseTP$rowname)
#write out to csv
write.csv(pre.post.pairwiseTP,
file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_pairwise_trophic_positions.csv",
row.names = F)
#alpha
#extract modes and credible intervals
pre.post.alpha <- sapply(pre.post.models$Alphas, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>%
as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>%
separate(rowname, into = c("Time", "Invasion", "Species"), sep = "\\.") %>%
unite(col = "Invasion Status", c(Time, Invasion), sep = "-") %>%
arrange(Species)
#pairwise comparisons
pre.post.pairwisealpha <- pairwiseComparisons(pre.post.models$Alphas) %>% as.data.frame() %>% rownames_to_column()
colnames(pre.post.pairwisealpha) <- c("Species", pre.post.pairwisealpha$rowname)
#write out to csv
write.csv(pre.post.pairwisealpha,
file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_pairwise_alpha.csv",
row.names = F)5.2 Pre- vs Post-Invasion Plots
#change Species names to full scientific names for lowest taxonomic level
pre.post.alpha.mean.ci <- pre.post.alpha %>% arrange(Species) %>% #select(Species, `2.5%`:`97.5%`) %>%
rename(alpha_2.5 = `2.5%`, alpha_50 = `50%`, alpha_97.5 = `97.5%`)
pre.post.tp.alpha.plot <- pre.post.trophic.positions %>% arrange(Species) %>%
rename(TP_2.5 = `2.5%`, TP_50 = `50%`, TP_97.5 = `97.5%`) %>%
bind_cols(select(pre.post.alpha.mean.ci, alpha_2.5:alpha_97.5)) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "Yellow Bullhead Catfish",
Species == "ANISOP" ~ "Dragonfly Larvae",
Species == "BELSPP" ~ "Giant Water Bug",
Species == "CICURO" ~ "Mayan Cichlid",
Species == "ERISUC" ~ "Lake Chubsucker",
Species == "FUNCHR" ~ "Golden Topminnow",
Species == "GAMHOL" ~ "Eastern Mosquitofish",
Species == "HETFOR" ~ "Least Killifish",
Species == "LEPMAC" ~ "Bluegill",
Species == "LEPMIC" ~ "Redear Sunfish",
Species == "LEPPUN" ~ "Spotted Sunfish",
Species == "PALPAL" ~ "Grass Shrimp",
Species == "POELAT" ~ "Sailfin Molly",
Species == "PROFAL" ~ "Slough Crayfish"
)) %>% as.data.frame()
#trophic position per species per habitat-season-slough
windowsFonts(Times = windowsFont("Times New Roman"))
(pre.post.tp.plot <- ggplot(data = pre.post.tp.alpha.plot %>%
mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(alpha_50))),
aes(x = Species, y = TP_50, color = `Invasion Status`, fill = `Invasion Status`))+
geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 0.75)+
geom_point(size = 1, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
scale_y_continuous(limits = c(1.5, 6.2), expand = c(0,0))+
scale_x_discrete(limits = c("Grass Shrimp", "Eastern Mosquitofish", "Least Killifish",
"Golden Topminnow", "Sailfin Molly", "Dragonfly Larvae",
"Giant Water Bug", "Spotted Sunfish", "Mayan Cichlid",
"Slough Crayfish", "Bluegill", "Redear Sunfish",
"Yellow Bullhead Catfish", "Lake Chubsucker"
))+
ylab("Trophic Position")+
xlab(NULL)+
annotate("text", x = 1, y = 4.75, label = "*", size = 3)+
annotate("text", x = 2, y = 4.75, label = "*", size = 3)+
annotate("text", x = 3, y = 4.75, label = "*", size = 3)+
annotate("text", x = 4, y = 4.75, label = "*", size = 3)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
panel.grid.minor = element_line(size = 0.5), panel.grid.major = element_line(size = 0.5),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_blank(),
axis.title = element_text(size = 5), axis.text.x.bottom = element_blank(),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 4))
)
# ggsave(filename = "pre_post_trophic_position.tiff",
# plot = pre.post.tp.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
#make the same plot but for alpha instead of trophic position
(pre.post.alpha.plot <- ggplot(data = pre.post.tp.alpha.plot %>%
mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(`Invasion Status`))),
aes(x = Species, y = alpha_50, color = `Invasion Status`, fill = `Invasion Status`))+
geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5, position = position_dodge(0.3), width = 0.75)+
geom_point(size = 1, position = position_dodge(0.3), shape = 21)+
scale_color_viridis_d(end = 0.75) +
scale_fill_viridis_d(end = 0.75) +
#scale_y_continuous(expand = c(0,0))+
scale_x_discrete(limits = c("Grass Shrimp", "Eastern Mosquitofish", "Least Killifish",
"Golden Topminnow", "Sailfin Molly", "Dragonfly Larvae",
"Giant Water Bug", "Spotted Sunfish", "Mayan Cichlid",
"Slough Crayfish", "Bluegill", "Redear Sunfish",
"Yellow Bullhead Catfish", "Lake Chubsucker"
))+
ylab("Alpha (Ranges from 0, 100% green algae, to 1, 100% detritus)")+
xlab(NULL)+
annotate("text", x = 2, y = 1.025, label = "*", size = 3)+
annotate("text", x = 3, y = 1.025, label = "*", size = 3)+
annotate("text", x = 8, y = 1.025, label = "*", size = 3)+
annotate("text", x = 12, y = 1.025, label = "*", size = 3)+
annotate("text", x = 14, y = 1.025, label = "*", size = 3)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.5), panel.grid.major = element_line(size = 0.5),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 4),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_blank(), legend.text = element_text(size = 4))
)
# ggsave(filename = "pre_post_alpha.tiff",
# plot = pre.post.alpha.plot,
# width = 174, height = 60, units = "mm", dpi = 600)
#combine into a single plot with shared legend
(pre.post.tp.alpha.ggarrange <- ggarrange(pre.post.tp.plot, pre.post.alpha.plot, nrow = 2, ncol = 1, font.label = c(size = 4),
labels = "AUTO", legend = "right", common.legend = T))
# ggsave(filename = "pre_post_tp_alpha.tiff",
# plot = pre.post.tp.alpha.ggarrange,
# width = 84, height = 126, units = "mm", dpi = 600)5.3 Pre- vs Post-Invasion tables
#reformat pairwise tp
pre.post.direct.tp <- pre.post.pairwiseTP %>%
separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>%
filter(grepl("Pre", Time)) %>% select(-c(Model)) %>%
arrange(Species) %>%
unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Status_Species") %>%
t() %>% as.data.frame() %>% rownames_to_column(var = "Species") %>%
separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>%
filter(grepl("Post", Time)) %>% select(-c(Model)) %>%
arrange(Species) %>%
unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Status_Species") %>%
t() %>% as.matrix()
#reformat tp posterior modes
pre.post.tp.wide <- pre.post.tp.alpha.plot %>% select(`Invasion Status`, Species, contains("TP")) %>%
pivot_wider(names_from = `Invasion Status`, values_from = contains("TP")) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
unite("Pre-Invasion 95% C.I.", c(`TP_2.5_Pre-Invasion`, `TP_97.5_Pre-Invasion`), sep = " - ") %>%
unite("Post-Invasion 95% C.I.", c(`TP_2.5_Post-Invasion`, `TP_97.5_Post-Invasion`), sep = " - ") %>%
rename(`Pre-Invasion Mode` = `TP_50_Pre-Invasion`, `Post-Invasion Mode` = `TP_50_Post-Invasion`) %>%
select(Species, contains("Pre"), contains("Post"))
#merge pairwise probs and posterior modes for tp
pre.post.tp.table <- pre.post.direct.tp %>% diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.post.direct.tp)) %>%
rename(`Pre < Post` = ".") %>%
separate(Species, into = c("Time", "Invasion", "Species"), sep = "_") %>%
#mutate(Time = gsub("\\[[[0-9][0-9]] ", "", Time)) %>%
#unite("Invasion_Status", c(Time, Invasion), sep = "-") %>%
select(Species, `Pre < Post`) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "ANISOP" ~ "Anisoptera",
Species == "BELSPP" ~ "Belostoma spp.",
Species == "CICURO" ~ "C. urophthalmus",
Species == "ERISUC" ~ "E. sucetta",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "HETFOR" ~ "H. formosa",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "PALPAL" ~ "P. paludosus",
Species == "POELAT" ~ "P. latipinna",
Species == "PROFAL" ~ "P. fallax")) %>%
merge(., pre.post.tp.wide, by = "Species") %>%
mutate(`Percent Change` = ((`Post-Invasion Mode` - `Pre-Invasion Mode`) / `Pre-Invasion Mode`)*100) %>%
select(Species, contains("Pre-"), contains("Post-"), everything())
#proportion of comparisons that are statistically different
sum(pre.post.tp.table$`Pre < Post`>=0.95|pre.post.tp.table$`Pre < Post`<=0.05)/nrow(pre.post.tp.table)
#export to csv
write.csv(pre.post.tp.table,
file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_tp_table.csv",
row.names = F)
#now do the same for alpha
#reformat pairwise alpha
pre.post.direct.alpha <- pre.post.pairwisealpha %>%
separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>%
filter(grepl("Pre", Time)) %>% select(-c(Model)) %>%
arrange(Species) %>%
unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Status_Species") %>%
t() %>% as.data.frame() %>% rownames_to_column(var = "Species") %>%
separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>%
filter(grepl("Post", Time)) %>% select(-c(Model)) %>%
arrange(Species) %>%
unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>%
column_to_rownames(var = "Invasion_Status_Species") %>%
t() %>% as.matrix()
#reformat alpha posterior modes
pre.post.alpha.wide <- pre.post.tp.alpha.plot %>% select(`Invasion Status`, Species, contains("alpha")) %>%
pivot_wider(names_from = `Invasion Status`, values_from = contains("alpha")) %>%
mutate(across(where(is.numeric), round, digits = 2)) %>%
unite("Pre-Invasion 95% C.I.", c(`alpha_2.5_Pre-Invasion`, `alpha_97.5_Pre-Invasion`), sep = " - ") %>%
unite("Post-Invasion 95% C.I.", c(`alpha_2.5_Post-Invasion`, `alpha_97.5_Post-Invasion`), sep = " - ") %>%
rename(`Pre-Invasion Mode` = `alpha_50_Pre-Invasion`, `Post-Invasion Mode` = `alpha_50_Post-Invasion`) %>%
select(Species, contains("Pre"), contains("Post"))
#merge pairwise probs and posterior modes for alpha
pre.post.alpha.table <- pre.post.direct.alpha %>% diag() %>% as.data.frame() %>%
mutate(Species = rownames(pre.post.direct.alpha)) %>%
rename(`Pre < Post` = ".") %>%
separate(Species, into = c("Time", "Invasion", "Species"), sep = "_") %>%
#mutate(Time = gsub("\\[[[0-9][0-9]] ", "", Time)) %>%
#unite("Invasion_Status", c(Time, Invasion), sep = "-") %>%
select(Species, `Pre < Post`) %>%
mutate(Species = case_when(
Species == "AMENAT" ~ "A. natalis",
Species == "ANISOP" ~ "Anisoptera",
Species == "BELSPP" ~ "Belostoma spp.",
Species == "CICURO" ~ "C. urophthalmus",
Species == "ERISUC" ~ "E. sucetta",
Species == "FUNCHR" ~ "F. chrysotus",
Species == "GAMHOL" ~ "G. holbrooki",
Species == "HETFOR" ~ "H. formosa",
Species == "LEPMAC" ~ "L. macrochirus",
Species == "LEPMIC" ~ "L. microlophus",
Species == "LEPPUN" ~ "L. punctatus",
Species == "PALPAL" ~ "P. paludosus",
Species == "POELAT" ~ "P. latipinna",
Species == "PROFAL" ~ "P. fallax")) %>%
merge(., pre.post.alpha.wide, by = "Species") %>%
mutate(`Percent Change` = ((`Post-Invasion Mode` - `Pre-Invasion Mode`) / `Pre-Invasion Mode`)*100) %>%
select(Species, contains("Pre-"), contains("Post-"), everything())
#proportion of comparisons that statistically changed
sum(pre.post.alpha.table$`Pre < Post`>=0.95|pre.post.alpha.table$`Pre < Post`<=0.05)/nrow(pre.post.alpha.table)
write.csv(pre.post.alpha.table,
"Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_alpha_table.csv",
row.names = F)6 Isotope Biplots
#facet_grid post-invasion isotopes by slough, habitat, and season
iso.biplot.data.post.invasion <- all.isotopes %>%
filter(!is.na(d13C), !is.na(d15N), !is.na(Habitat), Habitat != "Sawgrass") %>%
mutate(size_class = case_when(
is.na(size_class) ~ .$Species, T ~ size_class),
Habitat = case_when(
Habitat %in% c("Mangrove", "Edge") ~ "NP",
T ~ Habitat)
) %>%
mutate(across(contains("d1"), as.numeric)) %>%
group_by(Species, Slough) %>%
summarize(across(c(d13C, d15N), mean))`summarise()` has grouped output by 'Species'. You can override using the `.groups` argument.windowsFonts(Times = windowsFont("Times New Roman"))
(iso.biplot.post.invasion <- ggplot(iso.biplot.data.post.invasion, aes(x = d13C, y = d15N))+
facet_wrap(~Slough)+
geom_text(aes(label = Species), size = 0.5)+
theme_bw()+
theme(text = element_text(family = "Times", size = 5),
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
strip.text.x = element_text(size = 5),
strip.text.y = element_text(size = 5),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)
# ggsave(plot = iso.biplot.post.invasion,
# filename = "iso_biplot_post_invasion.tiff",
# dpi = 600, width = 174, height = 60, units = "mm")
d13c.large <- filter(all.isotopes, d13C > -20) %>% arrange(desc(d13C))
palpal <- pre.post.invasion %>% filter(Species == "PALPAL")
lepmac <- pre.post.invasion %>% filter(Species == "LEPMAC")
pre.post.invasion.mean <- pre.post.invasion %>%
filter(d13C < -15.9) %>%
group_by(Community, Species) %>%
summarize(across(where(is.numeric), list(mean = mean, sd = sd))) %>%
arrange(Species) %>%
mutate(Name = case_when(
Species == "AMENAT" ~ "Yellow Bullhead",
Species == "ANISOP" ~ "Dragonfly Larvae",
Species == "BELSPP" ~ "Creeping Water Bug",
Species == "ERISUC" ~ "Lake Chubsucker",
Species == "FLOC" ~ "Flocculent Matter",
Species == "FUNCHR" ~ "Golden Topminnow",
Species == "GAMHOL" ~ "Eastern Mosquitofish",
Species == "HETFOR" ~ "Least Killifish",
Species == "LEPMAC" ~ "Bluegill",
Species == "LEPMIC" ~ "Redear",
Species == "LEPPUN" ~ "Spotted Sunfish",
Species == "PALPAL" ~ "Grass Shrimp",
Species == "POELAT" ~ "Sailfin Molly",
Species == "PROFAL" ~ "Slough Crayfish",
T ~ Species
))`summarise()` has grouped output by 'Community'. You can override using the `.groups` argument.pre.post.invasion %>%
group_by(Community, Species) %>%
tally() %>%
arrange(Species) %>%
pivot_wider(names_from = Community, values_from = n) %>%
write.csv("Outputs/Stable Isotopes/pre_post_invasion_sample_sizes.csv", row.names = F)
(
iso.biplot.pre.post.invasion <-
ggplot(data = pre.post.invasion.mean, aes(x = d13C_mean, y = d15N_mean))+
#facet_wrap(~Community, scales = "free")+
geom_errorbar(aes(ymin = d15N_mean - d15N_sd, ymax = d15N_mean + d15N_sd), linewidth = 0.25, color = "lightgrey")+
geom_errorbar(aes(xmin = d13C_mean - d13C_sd, xmax = d13C_mean + d13C_sd), linewidth = 0.25, color = "lightgrey")+
#geom_text_repel(aes(label = Species, color = Community), size = 1, max.overlaps = 15) +
geom_line(aes(group = Name), color = "grey")+
geom_text(aes(label = Name, color = Community), size = 2) +
#scale_x_continuous(limits = c(-35,-25))+
scale_color_viridis_d(end = 0.75, breaks = c("Pre-Invasion", "Post-Invasion"), direction = -1)+
coord_fixed()+
xlab(expression(paste(delta^{13}, "C (\u2030)")))+
ylab(expression(paste(delta^{15}, "N (\u2030)")))+
theme_bw() +
theme(
text = element_text(family = "Times", size = 5),
axis.text.x = element_text(
#angle = 90,
#vjust = 0.5,
#hjust = 1,
size = 8
),
strip.text.x = element_text(size = 8),
strip.text.y = element_text(size = 8),
#panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_line(size = 0.1),
axis.line = element_line(color = 'black', size = 0.1),
axis.ticks.x = element_line(size = 0.05),
axis.title = element_text(size = 8),
axis.text.x.bottom = element_text(size = 8),
axis.text.y = element_text(size = 8),
axis.ticks.y = element_line(size = 0.2),
legend.title = element_text(size = 8),
legend.text = element_text(size = 8)
)
)
# ggsave(
# plot = iso.biplot.pre.post.invasion,
# filename = "iso_biplot_pre_post_invasion.tiff",
# dpi = 600, width = 200, height = 150, units = "mm"
# )---
title: "06_trophic_positions_from_stable_isotopes"
author: "Peter Flood"
date: "2024-03-04"
output: 
  html_document:
    toc: true
    df_print: paged
    number_sections: true
  html_notebook:
    toc: true
    toc_float: true
    number_sections: true
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

# Load libraries
```{r}
library(tidyverse) #for data manipulation and plotting via ggplot2
library(tRophicPosition) #Bayesian models of trophic position for stable isotopes
library(openxlsx) #importing and exporting Excel files
library(ggpubr) #arranging multiple ggplots
library(ggrepel) #make text not overlap during plotting
```

# Load and format post-invasion data
```{r}
#read in data
all.isotopes <- read.csv(file = "Data/Stable Isotopes/isotope_merge_all.csv", header = T)
```

Look at sample sizes per species size class per habitat per season per slough
```{r}
#sample sizes for each species
#what are the NA samples?
na.samples <- filter(all.isotopes, is.na(Habitat), is.na(Slough), is.na(Season))

sample.sizes <- all.isotopes %>% 
  filter(!is.na(d15N), !is.na(d13C), !is.na(Habitat)) %>% 
  mutate(size_class = case_when(is.na(size_class) ~ .$Species, T ~ size_class),
                                        Habitat = case_when(Habitat %in% c("Mangrove", "Edge") ~ "NP", T ~ Habitat)) %>% 
  group_by(size_class, Slough, Habitat, Season) %>% tally() %>% 
  pivot_wider(names_from = c(Slough, Habitat, Season), values_from = n) %>% 
  rename(`Size Class` = size_class) %>% 
  select(`Size Class`, SRS_Marsh_Wet, SRS_NP_Wet, SRS_Pond_Wet,
                       SRS_Marsh_Dry, SRS_NP_Dry, SRS_Pond_Dry,
                       TSL_Marsh_Wet, TSL_NP_Wet, TSL_Pond_Wet,
                       TSL_Marsh_Dry, TSL_NP_Dry, TSL_Pond_Dry)
```

Some data exploration and formatting
```{r}
#filter out extraneous habitat types & NAs
pond.np.marsh <- filter(all.isotopes, Habitat %in% c("Pond", "NP", "Marsh"), !is.na(d15N), !is.na(d13C))


#create group column to use in modelling function
pond.np.marsh <- pond.np.marsh %>% mutate(Community = paste(Slough, "-", Habitat, "-", Season, sep = ""))

#calculate d15N range for each community
d15n.range <- pond.np.marsh %>% group_by(Community) %>%
  summarise(
    Max = max(d15N),
    Min = min(d15N),
    Range = max(d15N) - min(d15N),
    Mean = mean(d15N),
    Efficiency = (max(d15N) - min(d15N)) / (max(d15N) / 3.4)
  ) %>%
  mutate(across(where(is.numeric), round, 2))
#select columns I'll need for the analysis
model.data <- pond.np.marsh %>% select(Species, d13C, d15N, Community, Sample_Type) %>% 
  mutate(Species = gsub("[\\**]", "", Species))
```

# Load and format pre-invasion data
```{r}
pre.invasion.isotopes <- read.csv(file = "Data/Stable Isotopes/FCE1263_Food_Webs_2023_manuscript_stable_isotope_data.csv")

pre.invasion.n2 <- pre.invasion.isotopes %>% group_by(Species) %>% filter(n() > 2)

#Loftus 2000 dissertation did not collect floc for stable isotope analysis
#create floc baseline group based on values from Williams and Trexler 2006
#Williams and Trexler is the closest temporally and also from the same region of the Everglades
#creating dataframe that uses mean and sd for d13C and d15N for floc from that paper
wt.floc <- data.frame(meand15N = 2.411579, SDd15N = 0.9437706,
                      meand13C = -29.02444, SDd13C = 1.813519)

#simulate pre-invasion floc data based on wt mean and sd
pre.invasion.floc <- data.frame(d15N = c(rnorm(mean = wt.floc$meand15N, sd = wt.floc$SDd15N, n = 20)),
                                d13C = c(rnorm(mean = wt.floc$meand13C, sd = wt.floc$SDd13C, n = 20)),
                                Species = "FLOC",
                                Sample_Type = "b1") 

#create pre-invasion green algae group
pre.invasion.green.algae <- data.frame(d15N = c(rnorm(mean = 3.9, sd = 0.422, n = 20)),
                                       d13C = c(rnorm(mean = -31.9, sd = 1.12, n = 20)),
                                       Species = "Green Algae",
                                       Sample_Type = "b2")

#Green Algae
#Simulating data based on values from Loftus's dissertation/ my first chapter
green.algae.C<-rnorm(n = 240, mean = -31.9, sd = 1.12) %>% data.frame()
green.algae.N<-rnorm(n = 240, mean = 3.9, sd = 0.422) %>% data.frame()
green.algae<-bind_cols(green.algae.C, green.algae.N)
colnames(green.algae)<-c("d13C", "d15N")
green.algae$Species<-"Green Algae"
green.algae$Community <- rep(c("TSL-Marsh-Wet", "TSL-NP-Wet", "TSL-Pond-Wet", 
                           "TSL-Marsh-Dry", "TSL-NP-Dry", "TSL-Pond-Dry",
                           "SRS-Marsh-Wet", "SRS-NP-Wet", "SRS-Pond-Wet",
                           "SRS-Marsh-Dry", "SRS-NP-Dry", "SRS-Pond-Dry"),
                         each = 20)
green.algae$Sample_Type <- "b2"

#check floc sample size among habitat-season levels for both sloughs
floc.n <- model.data %>% filter(Species == "FLOC") %>% 
  group_by(Community) %>% tally()

#There is no TSL-NP-Dry floc
#will need to simulate some data here too
#let's see how variable floc values are first
floc <- filter(all.isotopes, Species == "FLOC")

#summarize by communities
floc.iso.summary <- model.data %>% filter(Species == "FLOC") %>% group_by(Community) %>% 
  summarise(meand15N = mean(d15N), meand13C = mean(d13C),
            sdd15N = sd(d15N), sdd13C  = sd(d13C))

#mean is extrapolate from the difference between ponds and NP in the wet season
#sd is the mean sd for floc across all habitat season levels
#mean d15N
floc.iso.summary[10,2] - (floc.iso.summary[11,2]-floc.iso.summary[9,2])
#sd d15N
mean(floc.iso.summary$sdd15N)

#mean d13C
floc.iso.summary[10,3] - (floc.iso.summary[11,3]-floc.iso.summary[9,3])

#sd d13C
mean(floc.iso.summary$sdd13C)

#simulate data
floc.n.tsl.np.dry <- rnorm(n = 15, mean = 3.36, sd = 0.631) %>% data.frame() 
floc.c.tsl.np.dry <- rnorm(n = 15, mean = -25.4, sd = 1.59) %>% data.frame()
floc.tsl.np.dry <- bind_cols(floc.n.tsl.np.dry, floc.c.tsl.np.dry)
colnames(floc.tsl.np.dry) <- c("d15N", "d13C")
floc.tsl.np.dry <- floc.tsl.np.dry %>% mutate(Species = "FLOC", Sample_Type = "b1", 
                                              Community = "TSL-NP-Dry")

#combine with the rest of the data and filter for Species where n > 2
model.data.full <- bind_rows(floc.tsl.np.dry, green.algae, model.data) %>% group_by(Species, Community) %>% 
  filter(n() > 2) %>% as.data.frame()

#change sample_type for floc to b1
model.data.full$Sample_Type[model.data.full$Species=="FLOC"] <- "b1"

#filter for post-invaison data that matches habitat-season-slough with pre-invasion data
#pre-invasion isotopes are all from SRS wet-season marsh
post.srs.marsh.wet <- model.data.full %>% filter(Community == "SRS-Marsh-Wet")

#combine together baseline values with the rest of the isotope data
pre.invasion.baselines <- pre.invasion.n2 %>%  
  bind_rows(pre.invasion.floc, pre.invasion.green.algae) %>% 
  #change Species to match codes in model.data.full object
  mutate(Species = case_when(
    Species == "AMEIURUS NATALIS" ~ "AMENAT",
    Species == "ANISOPTERA NAIAD" ~ "ANISOP", 
    Species == "BACOPA" ~ "BACCAR", 
    Species == "BELONESOX" ~ "BELBEL", 
    Species == "BELOSTOMA" ~ "BELSPP",
    Species == "CICHLASOMA UROPHTHALMUS" ~ "CICURO", 
    Species == "CLADOCERA" ~ "CLADOC",
    Species == "ELEOCHARIS CELLULOSA" ~ "ELECEL", 
    Species == "ERIMYZON" ~ "ERISUC", 
    Species == "FUNDULUS CHRYSOTUS" ~ "FUNCHR",
    Species == "GAMBUSIA HOLBROOKI " ~ "GAMHOL", 
    Species == "HETERANDRIA" ~ "HETFOR",
    Species == "LEPISOSTEUS" ~ "LEPPLA", 
    Species == "LEPOMIS GULOSUS" ~ "LEPGUL",
    Species == "LEPOMIS MACROCHIRUS" ~ "LEPMAC",
    Species == "LEPOMIS MICROLOPHUS" ~ "LEPMIC",
    Species == "LEPOMIS PUNCTATUS" ~ "LEPPUN",
    Species == "MICROPTERUS" ~ "MICSAL", 
    Species == "NYMPHEA ODORATA" ~ "NYMODO",
    Species == "PALAEMONETES PALUDOSUS" ~ "PALPAL",
    Species == "PANICUM HEMITOMON" ~ "PANHEM",
    Species == "PASPALIDIUM GEMINATUM" ~ "PASGEM", 
    Species == "POECILIA" ~ "POELAT", 
    Species == "PONTEDARIA CORDATA" ~ "PONCOR", 
    Species == "PROCAMBARUS FALLAX" ~ "PROFAL",
    Species == "RHYNCHOSPORA TRACYI" ~ "RHYTRA",
    Species == "SAGITTARIA LANCIFOLIA" ~ "SAGLAN",
    Species == "TAPHROMYSIS LOUISIANAE" ~ "TAPLOU",
    Species == "TILAPIA MARIAE" ~ "TILMAR", 
    Species == "UTRICULARIA FOLIOSA" ~ "UTRFOL",
    Species == "FLOC" ~ "FLOC",
    Species == "Green Algae" ~ "Green Algae"
  )) %>% 
  mutate(Sample_Type = case_when(
    Code %in% c(7:11) ~ "vert",
    Code %in% c(4:7, 13) ~ "invert",
    Code == 3 ~ "om",
    Species %in% c("Green Algae", "FLOC") ~ Sample_Type
  )) %>% select(-c(Code)) %>% 
  mutate(Community = "Pre-Invasion") %>% 
  filter(Species %in% c(post.srs.marsh.wet$Species, "ANISOP"))


post.invasion.isotopes <- post.srs.marsh.wet %>% 
  mutate(Species = case_when(
    Species %in% c("BRAGRA", "CELSPP", "CORING", "ERYSIM", "PACLON") ~ "ANISOP",
    !Species %in% c("BRAGRA", "CELSPP", "CORING", "ERYSIM", "PACLON") ~ Species
  )) %>% 
  filter(Species %in% c(pre.invasion.baselines$Species)) %>% 
  mutate(Community = "Post-Invasion")

pre.post.invasion <- bind_rows(pre.invasion.baselines, post.invasion.isotopes) %>% 
  as.data.frame()

#write this out to excel to use in SIBER 
write.csv(pre.post.invasion, 
          file = "Outputs/Stable Isotopes/Pre-vs-Post-Invasion/pre_post_invasion_isotopes.csv", 
          row.names = F)
```

# Post-Invasion tRophicPosition models
```{r}
everglades.list <- extractIsotopeData(model.data.full, b1 = "b1", b2 = "b2", baselineColumn = "Sample_Type",
                                      consumersColumn = "Species", groupsColumn = "Community", 
                                      d13C = "d13C", d15N = "d15N")

for (community in everglades.list) {
  print(summary(community))
  plot(community)
}

everglades.models <- multiSpeciesTP(everglades.list, model = "twoBaselinesFull",
                                    n.adapt = 10000, n.iter = 10000,
                                    burnin = 1000, thin = 10, chains = 2, print = F)

#plot communities
slough.habitat.season.tp.alpha <- credibilityIntervals(everglades.models$df, x = "group", xlab = "Community")
```

## Extract model output
```{r}
#trophic position
#extract modes and credible intervals
trophic.positions <- sapply(everglades.models$TPs, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>% 
  as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>% 
  separate(rowname, into = c("Slough", "Habitat", "Season", "Species"), sep = "\\.") %>% 
  arrange(Slough, Habitat, Season, `50%`)
#write out to csv
write.csv(trophic.positions, 
          file = "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/trophic_position_mode_ci.csv",
          row.names = F)

#pairwise comparisons of trophic position
pairwiseTP <- pairwiseComparisons(everglades.models$TPs) %>% as.data.frame() %>% rownames_to_column()
colnames(pairwiseTP) <- c("Species", pairwiseTP$rowname)

#write out to csv
write.csv(pairwiseTP, 
          file = "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/pairwise_trophic_positions.csv",
          row.names = F)

#alpha
#extract modes and credible intervals
alpha <- sapply(everglades.models$Alphas, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>% 
  as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>% 
  separate(rowname, into = c("Slough", "Habitat", "Season", "Species"), sep = "\\.") %>% 
  arrange(Slough, Habitat, Season, `50%`)

write.csv(alpha, 
          file = "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/alpha_mode_ci.csv",
          row.names = F)

#pairwise comparisons
pairwisealpha <- pairwiseComparisons(everglades.models$Alphas) %>% as.data.frame() %>% rownames_to_column()
colnames(pairwisealpha) <- c("Species", pairwisealpha$rowname)

#write out to excel
write.csv(pairwisealpha, 
          file = "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/pairwise_alpha.csv",
          row.names = F)
```

## Plot post-invasion trophic positions

Format data
```{r}
#change Species names to full scientific names for lowest taxonomic level 
alpha.mean.ci <- alpha %>% arrange(Species, Slough, Habitat, Season) %>% #select(Species, `2.5%`:`97.5%`) %>% 
  rename(alpha_2.5 = `2.5%`, alpha_50 = `50%`, alpha_97.5 = `97.5%`)

tp.alpha.plot <- trophic.positions %>% arrange(Species, Slough, Habitat, Season) %>% 
  rename(TP_2.5 = `2.5%`, TP_50 = `50%`, TP_97.5 = `97.5%`) %>% 
  bind_cols(select(alpha.mean.ci, alpha_2.5:alpha_97.5)) %>% 
  filter(!Species %in% c("CLAJAM", "ELECEL", "PEREPI", "PERMAT", "PONCOR", "SAGLAN", "UTRFOL")) %>% 
  mutate(Species = case_when(
      Species == "ACRIPT" ~ "Acris gryllus - tadpole",
      Species == "ALGBAL" ~ "Nostoc spp.",
      Species == "AMENAT" ~ "Ameiurus natalis",
      Species == "AMICAL" ~ "Amia calva", 
      Species == "AMPHIPOD" ~ "Amphipoda",
      Species == "AMPMEA" ~ "Amphiuma means",
      Species == "APHSAY" ~ "Aphredoderus sayanus",
      Species == "BELBEL" ~ "Belonesox belizanus",
      Species == "BELSPP" ~ "Belostoma spp.",
      Species == "BLUE" ~ "Hydrachnidia", 
      Species == "BRAGRA" ~ "Brachymesia gravida",
      Species == "CELSPP" ~ "Celithemis spp.",
      Species == "CHIRON" ~ "Chironomidae", 
      Species == "CICBIM" ~ "Cichlasoma bimaculatum",
      Species == "CICMAN" ~ "Cichlasoma managuense",
      Species == "CICURO" ~ "Mayaheros urophthalmus", 
      Species == "CLABAT" ~ "Clarias batrachus",
      Species == "CLADOCERA" ~ "Cladocera", 
      #Species == "CLAJAM" ~ "C. jamaicense", 
      Species == "COENAG" ~ "Coenagrionidae", 
      Species == "COLEOA" ~ "Coleoptera", 
      Species == "COPEPOD" ~ "Copepoda", 
      Species == "CORING" ~ "Coryphaeschna ingens",
      Species == "CORIXI" ~ "Corixidae",
      Species == "ELAEVE" ~ "Elassoma evergladei", 
      #Species == "ELECEL" ~ "E. cellulosa", 
      Species == "ENNGLO" ~ "Enneacanthus gloriosus", 
      Species == "EPHEME" ~ "Ephemeroptera", 
      Species == "ERISUC" ~ "Erimyzon sucetta", 
      Species == "ERYSIM" ~ "Erythemis simplicicollis", 
      Species == "FUNCHR" ~ "Fundulus chrysotus", 
      Species == "FUNCON" ~ "Fundulus confluentus", 
      Species == "GAMHOL" ~ "Gambusia holbrooki", 
      Species == "GERRID" ~ "Gerridae", 
      Species == "HEMLET" ~ "Rubrachatochromis letourneuxi", 
      Species == "HETFOR" ~ "Heterandria formosa", 
      Species == "HOPLIT" ~ "Hoplisternum littorale", 
      Species == "JORFLO" ~ "Jordanella floridae", 
      Species == "LABSIC" ~ "Labidesthes vanhyningi", 
      Species == "LEPGUL" ~ "Lepomis gulosus", 
      Species == "LEPMAC" ~ "Lepomis macrochirus", 
      Species == "LEPMAR" ~ "Lepomis marginatus", 
      Species == "LEPMIC" ~ "Lepomis microlophus", 
      Species == "LEPPLA" ~ "Lepisosteus platyrhincus", 
      Species == "LEPPUN" ~ "Lepomis punctatus", 
      Species == "LIBINC" ~ "Libellula incesta", 
      Species == "LUCGOO" ~ "Lucania goodei", 
      Species == "MACSIA" ~ "Macrognathus siamensis", 
      Species == "MELTUB" ~ "Melanoides tuberculata", 
      Species == "MENBER" ~ "Menidia berylina", 
      Species == "MICSAL" ~ "Micropterus salmoides", 
      Species == "MONALB" ~ "Monopterus albus/javaensis", 
      Species == "NERFLO" ~ "Nerodia floridana", 
      Species == "NOTGYR" ~ "Noturus gyrinus", 
      Species == "NOTPET" ~ "Notropis petersoni", 
      Species == "NOTVIR" ~ "Notophthalmus viridescens", 
      Species == "OREAUR" ~ "Oreochromis aureus", 
      Species == "OSTRACOD" ~ "Ostracoda", 
      Species == "PACLON" ~ "Pachydiplax longipennis", 
      Species == "PALPAL" ~ "Palaemonetes paludosus", 
      Species == "PELFEM" ~ "Pelocoris femoratus", 
      #Species == "PEREPI" ~ "Periphyton - Epiphytic", 
      #Species == "PERMAT" ~ "Periphyton - Mat", 
      Species == "PLASCA" ~ "Planorbella spp.", 
      Species == "POELAT" ~ "Poecilia latipinna", 
      #Species == "PONCOR" ~ "P. cordata",
      Species == "PROALL" ~ "Procambarus alleni", 
      Species == "PROFAL" ~ "Procambarus fallax", 
      Species == "PROSPP" ~ "Procambarus juveniles", 
      Species == "RANATP" ~ "Rana grylio - tadpoles", 
      Species == "RANGRY" ~ "Rana grylio",
      #Species == "SAGLAN" ~ "S. lancifolia", 
      Species == "SIRLAC" ~ "Sirens lacertina", 
      Species == "SNOOK" ~ "Centropomis undecimalis", 
      Species == "TILMAR" ~ "Pelmetolapia mariae"
      #Species == "UTRFOL" ~ "U. foliosa"
      )) %>% 
        mutate(Habitat = case_when(Habitat == "NP" ~ "Near Pond",
                                   Habitat != "NP" ~ Habitat)) %>% 
  arrange(Slough, Season, Species, Habitat)
```

Trophic position per species per habitat-season-slough
```{r}
windowsFonts(Times = windowsFont("Times New Roman"))

(tp.current.plot <- ggplot(data = tp.alpha.plot, aes(x = Species, y = TP_50, fill = Season, color = Season, fill = Season))+
  facet_grid(Habitat ~ Slough)+
  geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
  geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
  scale_color_viridis_d(end = 0.75) +
  scale_fill_viridis_d(end = 0.75) +
  scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
  ylab("Trophic Position")+
  xlab(NULL)+
  theme_bw()+
  theme(text = element_text(family = "Times", size = 5),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
        strip.text.x = element_text(size = 5),
        strip.text.y = element_text(size = 5),
        #panel.border = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
        axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
        axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
        axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
        legend.title = element_text(size = 5), legend.text = element_text(size = 4))

)

# ggsave(filename = "trophic_position_habitat_season_slough.tiff", 
# plot = tp.current.plot, 
# width = 174, height = 60, units = "mm", dpi = 600)

```

### Seasonal comparisons
```{r}
#Format data
tp.seasonal.comparisons <- tp.alpha.plot %>%
  filter(
    #filter for species within a slough within a habtiat that have data from both seasons
    Species %in% c(
      "Mayaheros urophthalmus",
      "Celithemis spp.",
      "Elassoma evergladei",
      "Enneacanthus gloriosus",
      "Erimyzon sucetta",
      "Fundulus chyrsotus",
      "Gambusia holbrooki",
      "Heterandria formosa",
      "Anomalochromis letourneuxi",
      "Jordanella floridae",
      "Lucania goodei",
      "Lepomis marginatus",
      "Lepomis punctatus",
      "Pelcoris femoratus",
      "Palaemonetes paludosus"
    ) & Slough == "SRS" & Habitat == "Marsh" |
      Species %in% c(
        "Elassoma evergladei",
        "Fundulus chrysotus",
        "Gambusia holbrooki",
        "Heterandria formosa",
        "Lucania goodei",
        "Procambarus fallax",
        "Palaemonetes paludosus"
      ) &
      Slough == "SRS" & Habitat == "Near Pond" |
      Species %in% c(
        "Ameiurus natalis",
        "Amphipoda",
        "Belonesox belizanus",
        "Brachymesia gravida",
        "Mayaheros urophthalmus",
        "Celithemis spp.",
        "Copepoda",
        "Erimyzon sucetta",
        "Fundulus chrysotus",
        "Fundulus confluentus",
        "Gambusia holbrooki",
        "Heterandria formosa",
        "Jordanella floridae",
        "Lucania goodei",
        "Lepomis gulosus",
        "Lepomis marginatus",
        "Lepomis microlophus",
        "Lepisosteus platyrhincus",
        "Lepomis punctatus",
        "Procambarus alleni",
        "Procambarus fallax",
        "Poecilia latipinna",
        "Palaemonetes paludosus"
      ) &
      Slough == "SRS" & Habitat == "Pond" |
      Species %in% c(
        "Amphipoda",
        "Celithemis spp.",
        "Chironomidae",
        "Coenagrionidae",
        "Copepoda",
        "Corixidae",
        "Enneacanthus gloriosus",
        "Erythemis simpliciollis",
        "Ephemeroptera",
        "Fundulus chrysotus",
        "Gambusia holbrooki",
        "Gerridae",
        "Heterandria formosa",
        "Hydrachnidia",
        "Lucania goodei",
        "Pelocoris femoratus",
        "Palaemonetes paludosus"
      ) & Slough == "TSL" & Habitat == "Marsh" |
      Species %in% c("Amphipoda", "Cladocera", "Copepoda", "Hydrachnidia") &
      Slough == "TSL" & Habitat == "Near Pond" |
      Species %in% c(
        "Amphipoda",
        "Copepoda",
        "Gambusia holbrooki",
        "Heterandria formosa",
        "Lucania goodei",
        "Notropis petersoni",
        "Ostracoda",
        "Palaemonetes paludosus"
      ) &
      Slough == "TSL" & Habitat == "Pond"
  )

#Plot
(tp.seasonal.comparisons.plot <- ggplot(data = tp.seasonal.comparisons %>% 
                                          mutate(Species = fct_reorder(Species, TP_50),
                                                 Season = fct_rev(Season)), 
                                        aes(x = Species, y = TP_50, fill = Season, color = Season))+
    facet_grid(Habitat ~ Slough)+
    geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
    geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(begin = 0.2, end = 0.88) +
    scale_fill_viridis_d(begin = 0.2, end = 0.88) +
    scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
    ylab("Trophic Position")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 5), legend.text = element_text(size = 4))
  
)

# ggsave(filename = "trophic_position_seasonal_comparisons.tiff", 
       # plot = tp.seasonal.comparisons.plot, 
       # width = 174, height = 60, units = "mm", dpi = 600)
```

### Habitat comparisons
```{r}
#Format data
tp.habitat.comparisons <- tp.alpha.plot %>%
  filter(
    #filter for species within a slough within a season that have data from both seasons
    Species %in% c(
      "Belonesox belizanus",
      "Mayaheros urophthalmus",
      "Celithemis spp.",
      "Elassoma evergladei",
      "Enneacanthus gloriosus",
      "Erimyzon sucetta",
      "Fundulus chyrsotus",
      "Fundulus confluentus",
      "Gambusia holbrooki",
      "Heterandria formosa",
      "Hoplisternum littorale",
      "Jordanella floridae",
      "Lucania goodei",
      "Lepomis marginatus",
      "Lepomis punctatus",
      "Palaemonetes paludosus",
      "Planorbella spp.",
      "Poecilia latipinna",
      "Procambarus alleni",
      "Procambarus fallax",
      "Sirens lacertina"
    ) &
      Slough == "SRS" & Season == "Dry" |
      Species %in% c(
        "Anomalochromis letourneuxi",
        "Belostoma spp.",
        "Celithemis spp.",
        "Clarias batrachus",
        "Coenagrionidae",
        "Copepoda",
        "Elassoma evergladei",
        "Enneacanthus gloriosus",
        "Erimyzon sucetta",
        "Fundulus chrysotus",
        "Gambusia holbrooki",
        "Heterandria formosa",
        "Jordanella floridae",
        "Lepomis macrochirus",
        "Lepomis marginatus",
        "Lepomis microlophus",
        "Lepomis punctatus",
        "Libellula incesta",
        "Lucania goodei",
        "Macrognathus siamensis",
        "Mayaheros urophthalmus",
        "Oreochromis aureus",
        "Palaemonetes paludosus",
        "Pelocoris femoratus",
        "Poecilia latipinna",
        "Procambarus fallax"
      ) &
      Slough == "SRS" & Season == "Wet" |
      Species %in% c(
        "Amphipoda",
        "Chironomidae",
        "Cladocera",
        "Copepoda",
        "Gambusia holbrooki",
        "Heterandria formosa",
        "Hydrachnidia",
        "Lucania goodei",
        "Ostracoda",
        "Palaemonetes paludosus"
      ) &
      Slough == "TSL" & Season == "Dry" |
      Species %in% c(
        "Amphipoda",
        "Brachymesia gravida",
        "Celithemis spp.",
        "Chironomidae",
        "Cichlasoma bimaculatum",
        "Cichlasoma managuense",
        "Coenagrionidae",
        "Coleoptera",
        "Copepoda",
        "Enneacanthus gloriosus",
        "Erythemis simplicicollis",
        "Fundulus chrysotus",
        "Gambusia holbrooki",
        "Gerridae",
        "Heterandria formosa",
        "Hydrachnidia",
        "Lepomis marginatus",
        "Lucania goodei",
        "Mayaheros urophthalmus",
        "Monopterus albus/javensis",
        "Notropis petersoni",
        "Ostracoda",
        "Palaemonetes paludosus",
        "Pelocoris femoratus",
        "Planorbella spp."
      ) &
      Slough == "TSL" & Season == "Wet"
  )


(tp.habitat.comparisons.plot <- ggplot(data = tp.habitat.comparisons %>% 
                                          mutate(Species = fct_reorder(Species, TP_50),
                                                 Season = fct_rev(Season),
                                                 Habitat = fct_relevel(Habitat, c("Near Pond", "Marsh", "Pond"))), 
                                        aes(x = Species, y = TP_50, fill = Habitat, color = Habitat))+
    facet_wrap(~Slough*Season, ncol = 1)+
    geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
    geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
    coord_cartesian(ylim = c(1.5, 6))+
    ylab("Trophic Position")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 45, vjust = 1.05, hjust = 1, size = 5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', linewidth = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.75),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 5), legend.text = element_text(size = 4))
  
)

# ggsave(filename = "trophic_position_habitat_comparisons.tiff", 
#        plot = tp.habitat.comparisons.plot, 
#        width = 174, height = 120, units = "mm", dpi = 600)

```

## Plot post-invasion alpha
```{r}
tp.alpha.srs <- tp.alpha.plot %>% 
  filter(Slough == "SRS", Habitat != "NP") #%>% 
  #select(Habitat,Season, Species, TP_50, alpha_50) #%>% 
  #rename(`Trophic Position` = TP_50, Alpha = alpha_50) %>% 
  #pivot_longer(c("Trophic Position", "Alpha"), names_to = "Metric", values_to = "Median")

(tp.srs.plot <- ggplot(data = tp.alpha.plot %>% 
                               filter(Slough == "SRS", Habitat != "Near Pond"), 
                             aes(x = Species, y = TP_50, 
                                 color = Season, fill = Season))+
  facet_wrap(~Habitat)+
  geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, 
                position = position_dodge(0.3), width = 1)+
  geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
  scale_color_viridis_d(end = 0.75) +
  scale_fill_viridis_d(end = 0.75) +
  scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
  ylab("Trophic Position")+
  xlab(NULL)+
  theme_bw()+
  theme(text = element_text(family = "Times", size = 5),
        axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
        strip.text.x = element_text(size = 5),
        strip.text.y = element_text(size = 5),
        #panel.border = element_blank(), panel.grid.major = element_blank(),
        panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
        axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
        axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
        axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
        legend.title = element_text(size = 5), legend.text = element_text(size = 4))
  )

# ggsave(filename = "trophic_position_srs_post_isotopes.tiff", 
#        plot = tp.srs.plot,
#        width = 174, height = 60, units = "mm", dpi = 600)


(alpha.srs.plot <- ggplot(data = tp.alpha.plot %>% 
                         filter(Slough == "SRS", Habitat != "Near Pond"), 
                       aes(x = Species, y = alpha_50, 
                           color = Season, fill = Season))+
    facet_wrap(~Habitat)+
    geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5, 
                  position = position_dodge(0.3), width = 1)+
    geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(limits = c(1.5, 6), expand = c(0,0))+
    ylab("Alpha")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 5), legend.text = element_text(size = 4))
)


# ggsave(filename = "alpha_srs_post_isotopes.tiff", 
#        plot = alpha.srs.plot,
#        width = 174, height = 60, units = "mm", dpi = 600)

#make the same plot but for alpha instead of trophic position
(alpha.current.plot <- ggplot(data = tp.alpha.plot, aes(x = Species, y = alpha_50, color = Season, fill = Season))+
    facet_grid(Habitat ~ Slough)+
    geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5, position = position_dodge(0.3), width = 1)+
    geom_point(size = 0.5, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(expand = c(0,0))+
    ylab("Alpha")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 5), legend.text = element_text(size = 4))
  
)

# ggsave(filename = "alpha_habitat_season_slough.tiff", 
#        plot = alpha.current.plot, 
#        width = 174, height = 60, units = "mm", dpi = 600)

herp.tp.alpha <- tp.alpha.plot %>% 
  filter(Species %in% c("Acris gryllus - tadpole", "Nerodia floridana", "Notophthalmus viridescens", 
                        "Rana grylio", "Rana grylio - tadpole", "Sirens lacertina"))
  
(herp.tp.plot <- ggplot(data = herp.tp.alpha, aes(x = Species, y = TP_50, color = Habitat, shape = Season))+
    geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 1, position = position_dodge(0.3), width = 1)+
    geom_point(size = 3, position = position_dodge(0.3))+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(expand = c(0,0))+
    ylab("Trophic Position")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 10),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8),
          strip.text.x = element_text(size = 10),
          strip.text.y = element_text(size = 10),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 10), axis.text.x.bottom = element_text(size = 10, angle = 30, hjust = 0.5),
          axis.text.y = element_text(size = 10), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 10), legend.text = element_text(size = 10))
  
)

(herp.alpha.plot <- ggplot(data = herp.tp.alpha, aes(x = Species, y = alpha_50, color = Habitat, shape = Season))+
    geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 1, position = position_dodge(0.3), width = 1)+
    geom_point(size = 3, position = position_dodge(0.3))+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(expand = c(0,0))+
    ylab("Alpha")+
    xlab(NULL)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 8),
          strip.text.x = element_text(size = 10),
          strip.text.y = element_text(size = 10),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 10), axis.text.x.bottom = element_text(size = 10, angle = 30, hjust = 0.5),
          axis.text.y = element_text(size = 10), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 10), legend.text = element_text(size = 10))
  
)

#library(showtext)
#showtext::showtext_auto()
#showtext::showtext_opts(dpi = 600)
windowsFonts(Times = windowsFont("Times New Roman"))


herp.tp.alpha.plot <- ggarrange(herp.tp.plot, herp.alpha.plot, ncol = 1, nrow = 2, common.legend = T,
                                labels = "AUTO", legend = "right", font.label = list(family = "Times"),
                                hjust = -0.2)
herp.tp.alpha.plot

# ggsave("herp_tp_alpha_plot.tiff", 
#        plot = herp.tp.alpha.plot, 
#        dpi = 600, height = 150, width = 200, units = "mm")
```

## Format output into tables
### Trophic position tables
#### Seasonal comparisons
##### SRS marsh wet vs dry
```{r}
srs.marsh.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.marsh.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.marsh.season.direct.pairwise.tp <- srs.marsh.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.marsh.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.marsh.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS near pond wet vs dry
```{r}
srs.np.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.np.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.np.season.direct.pairwise.tp <- srs.np.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.np.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.np.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS Pond wet vs dry
```{r}
srs.pond.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.pond.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.pond.season.direct.pairwise.tp <- srs.pond.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.pond.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.pond.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL marsh wet vs dry
```{r}
tsl.marsh.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.marsh.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.marsh.season.direct.pairwise.tp <- tsl.marsh.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.marsh.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.marsh.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL near pond wet vs dry
```{r}
tsl.np.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.np.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.np.season.direct.pairwise.tp <- tsl.np.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.np.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.np.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL pond wet vs dry
```{r}
tsl.pond.season.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.pond.season.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.pond.season.direct.pairwise.tp <- tsl.pond.season.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.pond.season.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.pond.season.modes.tp) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine seasonal comparisons
```{r}
seasonal.pairwise.tp <- bind_rows(srs.marsh.season.direct.pairwise.tp, srs.np.season.direct.pairwise.tp, srs.pond.season.direct.pairwise.tp,
                               tsl.marsh.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax"
  ))

write.xlsx(seasonal.pairwise.tp, "seasonal_pairwise.tp.xlsx")

#proportion of changes that are statistically different
sum(seasonal.pairwise.tp$`Wet < Dry`>=0.95|seasonal.pairwise.tp$`Wet < Dry`<=0.05)/nrow(seasonal.pairwise.tp)

seasonal.pairwise.tp %>% 
  filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>% 
  mutate(deltaTP = abs(Wet - Dry)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

#break it down by habitat
#marsh
#frequency
sum(srs.marsh.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      srs.marsh.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
    tsl.marsh.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      tsl.marsh.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
  nrow(bind_rows(srs.marsh.season.direct.pairwise.tp, tsl.marsh.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.marsh.season.direct.pairwise.tp, tsl.marsh.season.direct.pairwise.tp) %>% 
  filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>% 
  mutate(deltaTP = abs(Wet - Dry)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

#np
#frequency
sum(srs.np.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      srs.np.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
    tsl.np.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      tsl.np.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
  nrow(bind_rows(srs.np.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.np.season.direct.pairwise.tp, tsl.np.season.direct.pairwise.tp) %>% 
  filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>% 
  mutate(deltaTP = abs(Wet - Dry)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

#pond
#frequency
sum(srs.pond.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      srs.pond.season.direct.pairwise.tp$`Wet < Dry`<=0.05,
    tsl.pond.season.direct.pairwise.tp$`Wet < Dry`>=0.95|
      tsl.pond.season.direct.pairwise.tp$`Wet < Dry`<=0.05)/
  nrow(bind_rows(srs.pond.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp))
#magnitude
bind_rows(srs.pond.season.direct.pairwise.tp, tsl.pond.season.direct.pairwise.tp) %>% 
  filter(`Wet < Dry` >= 0.95 | `Wet < Dry` <= 0.05) %>% 
  mutate(deltaTP = abs(Wet - Dry)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
```

#### Slough Comparisons
##### Marsh wet season
```{r}
slough.marsh.wet.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.marsh.wet.modes.tp <- trophic.positions %>% 
  filter(Season == "Wet", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.marsh.wet.direct.pairwise.tp <- slough.marsh.wet.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.wet.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.marsh.wet.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Near pond wet season
```{r}
slough.np.wet.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.np.wet.modes.tp <- trophic.positions %>% 
  filter(Season == "Wet", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.np.wet.direct.pairwise.tp <- slough.np.wet.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.np.wet.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.np.wet.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Pond wet season
```{r}
slough.pond.wet.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.pond.wet.modes.tp <- trophic.positions %>% 
  filter(Season == "Wet", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.pond.wet.direct.pairwise.tp <- slough.pond.wet.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.pond.wet.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.pond.wet.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Marsh dry season
```{r}
slough.marsh.dry.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.marsh.dry.modes.tp <- trophic.positions %>% 
  filter(Season == "Dry", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.marsh.dry.direct.pairwise.tp <- slough.marsh.dry.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.dry.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.marsh.dry.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Near pond dry season
```{r}
slough.np.dry.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.np.dry.modes.tp <- trophic.positions %>% 
  filter(Season == "Dry", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.np.dry.direct.pairwise.tp <- slough.np.dry.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.np.dry.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.np.dry.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Pond dry season
```{r}
slough.pond.dry.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
slough.pond.dry.modes.tp <- trophic.positions %>% 
  filter(Season == "Dry", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.pond.dry.direct.pairwise.tp <- slough.pond.dry.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.pond.dry.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.pond.dry.modes.tp) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine slough comparisons
```{r}
slough.pairwise.tp <- bind_rows(slough.marsh.wet.direct.pairwise.tp, slough.np.wet.direct.pairwise.tp, slough.pond.wet.direct.pairwise.tp,
                             slough.marsh.dry.direct.pairwise.tp, slough.np.dry.direct.pairwise.tp, slough.pond.dry.direct.pairwise.tp) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "TILMAR" ~ "T. mariae"
  ))

write.csv(slough.pairwise.tp, 
          "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/slough_pairwise.tp.csv", 
          row.names = F)

#proportion of changes that are statistically different
sum(slough.pairwise.tp$`SRS < TSL`>=0.95|slough.pairwise.tp$`SRS < TSL`<=0.05)/nrow(slough.pairwise.tp)

slough.pairwise %>% 
  filter(`SRS < TSL` >= 0.95 | `SRS < TSL` <= 0.05) %>% 
  mutate(deltaTP = abs(SRS - TSL)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))
```

#### Habitat comparisons
##### SRS wet marsh vs near pond
```{r}
srs.wet.marsh.np.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.wet.marsh.np.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
srs.wet.marsh.np.direct.pairwise.tp <- srs.wet.marsh.np.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.np.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., srs.wet.marsh.np.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS wet marsh vs pond
```{r}
srs.wet.marsh.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.wet.marsh.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
srs.wet.marsh.pond.direct.pairwise.tp <- srs.wet.marsh.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., srs.wet.marsh.pond.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS wet near pond vs pond
```{r}
srs.wet.np.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.wet.np.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
srs.wet.np.pond.direct.pairwise.tp <- srs.wet.np.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.np.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., srs.wet.np.pond.modes.tp) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet marsh vs near pond
```{r}
tsl.wet.marsh.np.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.marsh.np.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
tsl.wet.marsh.np.direct.pairwise.tp <- tsl.wet.marsh.np.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.np.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., tsl.wet.marsh.np.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet marsh vs pond
```{r}
tsl.wet.marsh.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.marsh.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
tsl.wet.marsh.pond.direct.pairwise.tp <- tsl.wet.marsh.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., tsl.wet.marsh.pond.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet near pond vs pond
```{r}
tsl.wet.np.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.wet.np.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
tsl.wet.np.pond.direct.pairwise.tp <- tsl.wet.np.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.np.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., tsl.wet.np.pond.modes.tp) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry marsh vs near pond
```{r}
srs.dry.marsh.np.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.dry.marsh.np.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
srs.dry.marsh.np.direct.pairwise.tp <- srs.dry.marsh.np.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.np.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., srs.dry.marsh.np.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry marsh vs pond
```{r}
srs.dry.marsh.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.dry.marsh.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
srs.dry.marsh.pond.direct.pairwise.tp <- srs.dry.marsh.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., srs.dry.marsh.pond.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry near pond vs pond
```{r}
srs.dry.np.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
srs.dry.np.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
srs.dry.np.pond.direct.pairwise.tp <- srs.dry.np.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.np.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., srs.dry.np.pond.modes.tp) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry marsh vs near pond
```{r}
tsl.dry.marsh.np.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.marsh.np.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
tsl.dry.marsh.np.direct.pairwise.tp <- tsl.dry.marsh.np.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.np.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., tsl.dry.marsh.np.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry marsh vs pond
```{r}
tsl.dry.marsh.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.marsh.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
tsl.dry.marsh.pond.direct.pairwise.tp <- tsl.dry.marsh.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., tsl.dry.marsh.pond.modes.tp) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry near pond vs pond
```{r}
tsl.dry.np.pond.pairwise.tp <- pairwiseTP %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.tp to bind w/ pairwise.tp probs
tsl.dry.np.pond.modes.tp <- trophic.positions %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
tsl.dry.np.pond.direct.pairwise.tp <- tsl.dry.np.pond.pairwise.tp %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.np.pond.pairwise.tp)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., tsl.dry.np.pond.modes.tp) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine habitat comparisons
```{r}
marsh.np.pairwise.tp <- bind_rows(srs.wet.marsh.np.direct.pairwise.tp, srs.dry.marsh.np.direct.pairwise.tp, 
                                  tsl.wet.marsh.np.direct.pairwise.tp, tsl.dry.marsh.np.direct.pairwise.tp) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "TILMAR" ~ "T. mariae"))

#proportion of statistically different comparisons for marsh-np
sum(marsh.np.pairwise.tp$`Marsh < NP`>=0.95|marsh.np.pairwise.tp$`Marsh < NP`<=0.05)/nrow(marsh.np.pairwise.tp)

marsh.np.pairwise.tp %>% 
  filter(`Marsh < NP` >= 0.95 | `Marsh < NP` <= 0.05) %>% 
  mutate(deltaTP = abs(Marsh - NP)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

marsh.pond.pairwise.tp <- bind_rows(srs.wet.marsh.pond.direct.pairwise.tp, srs.dry.marsh.pond.direct.pairwise.tp, 
                                    tsl.wet.marsh.pond.direct.pairwise.tp, tsl.dry.marsh.pond.direct.pairwise.tp) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLABAT" ~ "C. batrachus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "HOPLIT" ~ "H. littorale", 
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LIBINC" ~ "L. incesta", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OREAUR" ~ "O. aureus",
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "SIRLAC" ~ "S. lacertina",
    Species == "TILMAR" ~ "T. mariae"))

#proportion of statistically different comparisons for marsh-pond
sum(marsh.pond.pairwise.tp$`Marsh < Pond`>=0.95|marsh.pond.pairwise.tp$`Marsh < Pond`<=0.05)/nrow(marsh.pond.pairwise.tp)

marsh.pond.pairwise.tp %>% 
  filter(`Marsh < Pond` >= 0.95 | `Marsh < Pond` <= 0.05) %>% 
  mutate(deltaTP = abs(Marsh - Pond)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

np.pond.pairwise.tp <- bind_rows(srs.wet.np.pond.direct.pairwise.tp, srs.dry.np.pond.direct.pairwise.tp, 
                                 tsl.wet.np.pond.direct.pairwise.tp, tsl.dry.np.pond.direct.pairwise.tp) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "TILMAR" ~ "T. mariae"))


#proportion of statistically different comparisons for np-pond
sum(np.pond.pairwise.tp$`NP < Pond`>=0.95|np.pond.pairwise.tp$`NP < Pond`<=0.05)/nrow(np.pond.pairwise.tp)

np.pond.pairwise.tp %>% 
  filter(`NP < Pond` >= 0.95 | `NP < Pond` <= 0.05) %>% 
  mutate(deltaTP = abs(NP - Pond)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

#proportion of all habitat changes
(sum(marsh.np.pairwise.tp$`Marsh < NP`>=0.95|marsh.np.pairwise.tp$`Marsh < NP`<=0.05) +
    sum(marsh.pond.pairwise.tp$`Marsh < Pond`>=0.95|marsh.pond.pairwise.tp$`Marsh < Pond`<=0.05)+
      sum(np.pond.pairwise.tp$`NP < Pond`>=0.95|np.pond.pairwise.tp$`NP < Pond`<=0.05))/(
        nrow(marsh.np.pairwise.tp)+nrow(marsh.pond.pairwise.tp)+nrow(np.pond.pairwise.tp)
      )

bind_rows(
  marsh.np.pairwise.tp %>% rename(Comparison = `Marsh < NP`, A = Marsh, B = NP),
  marsh.pond.pairwise.tp %>% rename(Comparison = `Marsh < Pond`, A = Marsh, B = Pond),
  np.pond.pairwise.tp %>% rename(Comparison = `NP < Pond`, A = NP, B = Pond)
) %>% 
  filter(Comparison >= 0.95 | Comparison <= 0.05) %>% 
  mutate(deltaTP = abs(A - B)) %>% 
  summarise_at(.vars = "deltaTP", list(mean = mean, sd = sd))

habitat.pairwise.tp <- list("Marsh-NP" = marsh.np.pairwise.tp,
                         "Marsh-Pond" = marsh.pond.pairwise.tp,
                         "NP-Pond" = np.pond.pairwise.tp)

#export to Excel (has multiple tabs)
write.xlsx(habitat.pairwise.tp,
          "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/habitat_pairwise_tp.xlsx")
```

### Alpha tables
#### Seasonal comparisons
##### SRS marsh wet vs dry
```{r}
srs.marsh.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.marsh.season.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.marsh.season.direct.pairwise.alpha <- srs.marsh.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.marsh.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.marsh.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS near pond wet vs dry
```{r}
srs.np.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.np.season.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.np.season.direct.pairwise.alpha <- srs.np.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.np.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.np.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS pond wet vs dry
```{r}
srs.pond.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.pond.season.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
srs.pond.season.direct.pairwise.alpha <- srs.pond.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.pond.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., srs.pond.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL marsh wet vs dry
```{r}
tsl.marsh.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.marsh.season.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.marsh.season.direct.pairwise.alpha <- tsl.marsh.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.marsh.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.marsh.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL near pond wet vs dry
```{r}
tsl.np.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.np.season.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.np.season.direct.pairwise.alpha <- tsl.np.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.np.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.np.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL pond wet vs dry
```{r}
tsl.pond.season.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.pond.season.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Season, values_from = Mode) %>% 
  filter(!is.na(Dry), !is.na(Wet))

#combine these
tsl.pond.season.direct.pairwise.alpha <- tsl.pond.season.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.pond.season.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Wet < Dry` = ".") %>% 
  merge(., tsl.pond.season.modes.alpha) %>% 
  select(Species, Slough, Habitat, Wet, Dry, `Wet < Dry`, -c(Season)) %>% 
  mutate(`Percent Change` = ((Dry - Wet)/Wet)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine seasonal comparisons
```{r}
seasonal.pairwise.alpha <- bind_rows(srs.marsh.season.direct.pairwise.alpha, srs.np.season.direct.pairwise.alpha, srs.pond.season.direct.pairwise.alpha,
                                  tsl.marsh.season.direct.pairwise.alpha, tsl.np.season.direct.pairwise.alpha, tsl.pond.season.direct.pairwise.alpha) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax"
  ))

write.csv(seasonal.pairwise.alpha, 
          "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/seasonal_pairwise.alpha.csv",
          row.names = F)

#proportion of changes that are statistically different
sum(seasonal.pairwise.alpha$`Wet < Dry`>=0.95|seasonal.pairwise.alpha$`Wet < Dry`<=0.05)/nrow(seasonal.pairwise.alpha)
```

#### Slough comparisons
##### Marsh wet season
```{r}
slough.marsh.wet.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.marsh.wet.modes.alpha <- alpha %>% 
  filter(Season == "Wet", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.marsh.wet.direct.pairwise.alpha <- slough.marsh.wet.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.wet.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.marsh.wet.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Near pond wet season
```{r}
slough.np.wet.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.np.wet.modes.alpha <- alpha %>% 
  filter(Season == "Wet", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.np.wet.direct.pairwise.alpha <- slough.np.wet.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.np.wet.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.np.wet.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Pond wet season
```{r}
slough.pond.wet.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.pond.wet.modes.alpha <- alpha %>% 
  filter(Season == "Wet", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.pond.wet.direct.pairwise.alpha <- slough.pond.wet.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.pond.wet.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.pond.wet.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))

```

##### Marsh dry season
```{r}
slough.marsh.dry.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.marsh.dry.modes.alpha <- alpha %>% 
  filter(Season == "Dry", Habitat == "Marsh") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.marsh.dry.direct.pairwise.alpha <- slough.marsh.dry.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.marsh.dry.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.marsh.dry.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Near pond dry season
```{r}
slough.np.dry.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.np.dry.modes.alpha <- alpha %>% 
  filter(Season == "Dry", Habitat == "NP") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.np.dry.direct.pairwise.alpha <- slough.np.dry.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.np.dry.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.np.dry.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Pond dry season
```{r}
slough.pond.dry.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
slough.pond.dry.modes.alpha <- alpha %>% 
  filter(Season == "Dry", Habitat == "Pond") %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Slough, values_from = Mode) %>% 
  filter(!is.na(TSL), !is.na(SRS))

#combine these
slough.pond.dry.direct.pairwise.alpha <- slough.pond.dry.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(slough.pond.dry.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`SRS < TSL` = ".") %>% 
  merge(., slough.pond.dry.modes.alpha) %>% 
  select(Species, Season, Habitat, SRS, TSL, `SRS < TSL`, -c(Slough)) %>% 
  mutate(`Percent Change` = ((TSL - SRS)/SRS)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine slough comparisons
```{r}
slough.pairwise.alpha <- bind_rows(slough.marsh.wet.direct.pairwise.alpha, slough.np.wet.direct.pairwise.alpha, slough.pond.wet.direct.pairwise.alpha,
                                slough.marsh.dry.direct.pairwise.alpha, slough.np.dry.direct.pairwise.alpha, slough.pond.dry.direct.pairwise.alpha) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "TILMAR" ~ "T. mariae"
  ))

write.csv(slough.pairwise.alpha, 
          "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/slough_pairwise.alpha.csv",
          row.names = F)

#proportion of changes that are statistically different
sum(slough.pairwise.alpha$`SRS < TSL`>=0.95|slough.pairwise.alpha$`SRS < TSL`<=0.05)/nrow(slough.pairwise.alpha)
```

#### Habitat comparisons
##### SRS wet marsh vs near pond
```{r}
srs.wet.marsh.np.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.marsh.np.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
srs.wet.marsh.np.direct.pairwise.alpha <- srs.wet.marsh.np.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.np.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., srs.wet.marsh.np.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS wet marsh vs pond
```{r}
srs.wet.marsh.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.marsh.pond.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
srs.wet.marsh.pond.direct.pairwise.alpha <- srs.wet.marsh.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.marsh.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., srs.wet.marsh.pond.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS wet near pond vs pond
```{r}
srs.wet.np.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.wet.np.pond.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Wet", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
srs.wet.np.pond.direct.pairwise.alpha <- srs.wet.np.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.wet.np.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., srs.wet.np.pond.modes.alpha) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet marsh vs near pond
```{r}
tsl.wet.marsh.np.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.marsh.np.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
tsl.wet.marsh.np.direct.pairwise.alpha <- tsl.wet.marsh.np.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.np.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., tsl.wet.marsh.np.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet marsh vs pond
```{r}
tsl.wet.marsh.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.marsh.pond.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
tsl.wet.marsh.pond.direct.pairwise.alpha <- tsl.wet.marsh.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.marsh.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., tsl.wet.marsh.pond.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL wet near pond vs pond
```{r}
tsl.wet.np.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Wet") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Wet") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.wet.np.pond.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Wet", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
tsl.wet.np.pond.direct.pairwise.alpha <- tsl.wet.np.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.wet.np.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., tsl.wet.np.pond.modes.alpha) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry marsh vs near pond
```{r}
srs.dry.marsh.np.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.marsh.np.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
srs.dry.marsh.np.direct.pairwise.alpha <- srs.dry.marsh.np.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.np.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., srs.dry.marsh.np.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry marsh vs pond
```{r}
srs.dry.marsh.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.marsh.pond.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
srs.dry.marsh.pond.direct.pairwise.alpha <- srs.dry.marsh.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.marsh.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., srs.dry.marsh.pond.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### SRS dry near pond vs pond
```{r}
srs.dry.np.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "SRS", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
srs.dry.np.pond.modes.alpha <- alpha %>% 
  filter(Slough == "SRS", Season == "Dry", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
srs.dry.np.pond.direct.pairwise.alpha <- srs.dry.np.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(srs.dry.np.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., srs.dry.np.pond.modes.alpha) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry marsh vs near pond
```{r}
tsl.dry.marsh.np.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.marsh.np.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "NP")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(NP))

#combine these
tsl.dry.marsh.np.direct.pairwise.alpha <- tsl.dry.marsh.np.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.np.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < NP` = ".") %>% 
  merge(., tsl.dry.marsh.np.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, NP, `Marsh < NP`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((NP - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry marsh vs pond
```{r}
tsl.dry.marsh.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Marsh", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.marsh.pond.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("Marsh", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(Marsh), !is.na(Pond))

#combine these
tsl.dry.marsh.pond.direct.pairwise.alpha <- tsl.dry.marsh.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.marsh.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`Marsh < Pond` = ".") %>% 
  merge(., tsl.dry.marsh.pond.modes.alpha) %>% 
  select(Species, Slough, Season, Marsh, Pond, `Marsh < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - Marsh)/Marsh)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### TSL dry near pond vs pond
```{r}
tsl.dry.np.pond.pairwise.alpha <- pairwisealpha %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "NP", Season == "Dry") %>% 
  select(-c(Slough, Habitat, Season)) %>% 
  column_to_rownames(var = "Species") %>% 
  t() %>% as.data.frame() %>% 
  rownames_to_column(var = "Species") %>% 
  mutate(Species = gsub("\\[[[0-9]+] ", "", Species)) %>% 
  separate(Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "[\\.]") %>% 
  filter(Slough == "TSL", Habitat == "Pond", Season == "Dry") %>% 
  filter(Species %in% colnames(.)) %>% 
  select(Slough, Habitat, Season, Species, matches(.$Species)) %>% 
  unite(Slough_Habitat_Season_Species, c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  column_to_rownames(var = "Slough_Habitat_Season_Species")

#format modes.alpha to bind w/ pairwise.alpha probs
tsl.dry.np.pond.modes.alpha <- alpha %>% 
  filter(Slough == "TSL", Season == "Dry", Habitat %in% c("NP", "Pond")) %>% 
  rename(Mode = `50%`) %>% 
  pivot_wider(id_cols = Species, names_from = Habitat, values_from = Mode) %>% 
  filter(!is.na(NP), !is.na(Pond))

#combine these
tsl.dry.np.pond.direct.pairwise.alpha <- tsl.dry.np.pond.pairwise.alpha %>% as.matrix %>% 
  diag() %>% as.data.frame() %>% 
  mutate(Slough_Habitat_Season_Species = row.names(tsl.dry.np.pond.pairwise.alpha)) %>% 
  separate(Slough_Habitat_Season_Species, into = c("Slough", "Habitat", "Season", "Species"), sep = "_") %>% 
  rename(`NP < Pond` = ".") %>% 
  merge(., tsl.dry.np.pond.modes.alpha) %>% 
  select(Species, Slough, Season, NP, Pond, `NP < Pond`, -c(Habitat)) %>% 
  mutate(`Percent Change` = ((Pond - NP)/NP)*100) %>% 
  mutate(across(where(is.numeric), round, 2))
```

##### Combine habitat comparisons
```{r}
marsh.np.pairwise.alpha <- bind_rows(srs.wet.marsh.np.direct.pairwise.alpha, srs.dry.marsh.np.direct.pairwise.alpha, 
                                  tsl.wet.marsh.np.direct.pairwise.alpha, tsl.dry.marsh.np.direct.pairwise.alpha) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "TILMAR" ~ "T. mariae"))

#proportion of statistically different comparisons for marsh-np
sum(marsh.np.pairwise.alpha$`Marsh < NP`>=0.95|marsh.np.pairwise.alpha$`Marsh < NP`<=0.05)/nrow(marsh.np.pairwise.alpha)

marsh.pond.pairwise.alpha <- bind_rows(srs.wet.marsh.pond.direct.pairwise.alpha, srs.dry.marsh.pond.direct.pairwise.alpha, 
                                    tsl.wet.marsh.pond.direct.pairwise.alpha, tsl.dry.marsh.pond.direct.pairwise.alpha) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLABAT" ~ "C. batrachus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "HOPLIT" ~ "H. littorale", 
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LIBINC" ~ "L. incesta", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OREAUR" ~ "O. aureus",
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "SIRLAC" ~ "S. lacertina",
    Species == "TILMAR" ~ "T. mariae"))

#proportion of statistically different comparisons for marsh-pond
sum(marsh.pond.pairwise.alpha$`Marsh < Pond`>=0.95|marsh.pond.pairwise.alpha$`Marsh < Pond`<=0.05)/nrow(marsh.pond.pairwise.alpha)

np.pond.pairwise.alpha <- bind_rows(srs.wet.np.pond.direct.pairwise.alpha, srs.dry.np.pond.direct.pairwise.alpha, 
                                 tsl.wet.np.pond.direct.pairwise.alpha, tsl.dry.np.pond.direct.pairwise.alpha) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "AMPHIPOD" ~ "Amphipoda",
    Species == "BELBEL" ~ "B. belizanus",
    Species == "BELSPP" ~ "Belostoma spp",
    Species == "BLUE" ~ "Hydrachnidia",
    Species == "BRAGRA" ~ "B. gravida",
    Species == "CELSPP" ~ "Celithemus spp.",
    Species == "CHIRON" ~ "Chironomidae",
    Species == "CICBIM" ~ "C. bimaculatum",
    Species == "CICMAN" ~ "C. managuense",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "CLADOCERA" ~ "Cladocera", 
    Species == "COENAG" ~ "Coenagrionidae",
    Species == "COLEOA" ~ "Coleoptera",
    Species == "COPEPOD" ~ "Copepoda",
    Species == "CORIXI" ~ "Corixidae",
    Species == "ELAEVE" ~ "E. evergladei",
    Species == "ENNGLO" ~ "E. gloriosus",
    Species == "EPHEME" ~ "Ephemeroptera",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "ERYSIM" ~ "E. simplicicollis",
    Species == "FUNCHR" ~ "F. chrysotus",
    Species == "FUNCON" ~ "F. confluentus",
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "GERRID" ~ "Gerridae", 
    Species == "HEMLET" ~ "H. letourneuxi",
    Species == "HETFOR" ~ "H. formosa",
    Species == "JORFLO" ~ "J. floridae", 
    Species == "LEPGUL" ~ "L. gulosus",
    Species == "LEPMAC" ~ "L. macrochirus",
    Species == "LEPMAR" ~ "L. marginatus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPLA" ~ "L. platyrhincus",
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "LUCGOO" ~ "L. goodei", 
    Species == "MACSIA" ~ "M. siamensis",
    Species == "MICSAL" ~ "M. salmoides",
    Species == "MONALB" ~ "M. albus",
    Species == "NOTPET" ~ "N. petersoni", 
    Species == "OSTRACOD" ~ "Ostracoda", 
    Species == "PALPAL" ~ "P. paludosus",
    Species == "PELFEM" ~ "P. femoratus",
    Species == "PERMAT" ~ "Periphyton (Mat)",
    Species == "PLASCA" ~ "Planorbella spp.",
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROALL" ~ "P. alleni", 
    Species == "PROFAL" ~ "P. fallax",
    Species == "PROSPP" ~ "Procambarus spp.",
    Species == "TILMAR" ~ "T. mariae"))

#proportion of statistically different comparisons for np-pond
sum(np.pond.pairwise.alpha$`NP < Pond`>=0.95|np.pond.pairwise.alpha$`NP < Pond`<=0.05)/nrow(np.pond.pairwise.alpha)

#proportion of changes across all habitat comparisons
(
  sum(marsh.np.pairwise.alpha$`Marsh < NP`>=0.95|marsh.np.pairwise.alpha$`Marsh < NP`<=0.05)+
  sum(marsh.pond.pairwise.alpha$`Marsh < Pond`>=0.95|marsh.pond.pairwise.alpha$`Marsh < Pond`<=0.05)+
  sum(np.pond.pairwise.alpha$`NP < Pond`>=0.95|np.pond.pairwise.alpha$`NP < Pond`<=0.05)  
  
)/(
  nrow(marsh.np.pairwise.alpha)+nrow(marsh.pond.pairwise.alpha)+nrow(np.pond.pairwise.alpha)
)

habitat.pairwise.alpha <- list("Marsh-NP" = marsh.np.pairwise.alpha,
                            "Marsh-Pond" = marsh.pond.pairwise.alpha,
                            "NP-Pond" = np.pond.pairwise.alpha)

#export to Excel
write.xlsx(habitat.pairwise.alpha, 
           "Outputs/Stable Isotopes/Trophic Position/Post-Invasion/habitat_pairwise_alpha.xlsx")
```

# Pre- vs Post-Invasion tRophic Position Models
```{r}
pre.post.list <- extractIsotopeData(pre.post.invasion, b1 = "b1", b2 = "b2", baselineColumn = "Sample_Type",
                                    consumersColumn = "Species", groupsColumn = "Community",
                                    d13C = "d13C", d15N = "d15N")

for (community in pre.post.list) {
  print(summary(community))
  plot(community)
}

pre.post.models <- multiSpeciesTP(pre.post.list, model = "twoBaselinesFull",
                                  n.adapt = 10000, n.iter = 10000,
                                  burnin = 1000, thin = 10, chains = 2, print = F)

#plot communities
credibilityIntervals(pre.post.models$df, x = "group", xlab = "Community")
```

## Extract model output
```{r}
#trophic position
#extract modes and credible intervals
pre.post.trophic.positions <- sapply(pre.post.models$TPs, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>% 
  as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>% 
  separate(rowname, into = c("Time", "Invasion", "Species"), sep = "\\.") %>% 
  unite(col = "Invasion Status", c(Time, Invasion), sep = "-") %>% 
  arrange(Species)

#write out to csv
write.csv(pre.post.trophic.positions, 
          file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_trophic_position_mode_ci.csv",
          row.names = F)

#pairwise comparisons of trophic position
pre.post.pairwiseTP <- pairwiseComparisons(pre.post.models$TPs) %>% as.data.frame() %>% rownames_to_column()
colnames(pre.post.pairwiseTP) <- c("Species", pre.post.pairwiseTP$rowname)

#write out to csv
write.csv(pre.post.pairwiseTP, 
          file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_pairwise_trophic_positions.csv",
          row.names = F)

#alpha
#extract modes and credible intervals
pre.post.alpha <- sapply(pre.post.models$Alphas, quantile, probs = c(0.025, 0.5, 0.975)) %>% round(3) %>% 
  as.data.frame() %>% t() %>% as.data.frame() %>% rownames_to_column() %>% 
  separate(rowname, into = c("Time", "Invasion", "Species"), sep = "\\.") %>% 
  unite(col = "Invasion Status", c(Time, Invasion), sep = "-") %>% 
  arrange(Species)

#pairwise comparisons
pre.post.pairwisealpha <- pairwiseComparisons(pre.post.models$Alphas) %>% as.data.frame() %>% rownames_to_column()
colnames(pre.post.pairwisealpha) <- c("Species", pre.post.pairwisealpha$rowname)

#write out to csv
write.csv(pre.post.pairwisealpha, 
          file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_pairwise_alpha.csv",
          row.names = F)
```

## Pre- vs Post-Invasion Plots
```{r}
#change Species names to full scientific names for lowest taxonomic level 
pre.post.alpha.mean.ci <- pre.post.alpha %>% arrange(Species) %>% #select(Species, `2.5%`:`97.5%`) %>% 
  rename(alpha_2.5 = `2.5%`, alpha_50 = `50%`, alpha_97.5 = `97.5%`)

pre.post.tp.alpha.plot <- pre.post.trophic.positions %>% arrange(Species) %>% 
  rename(TP_2.5 = `2.5%`, TP_50 = `50%`, TP_97.5 = `97.5%`) %>% 
  bind_cols(select(pre.post.alpha.mean.ci, alpha_2.5:alpha_97.5)) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "Yellow Bullhead Catfish",
    Species == "ANISOP" ~ "Dragonfly Larvae",
    Species == "BELSPP" ~ "Giant Water Bug",
    Species == "CICURO" ~ "Mayan Cichlid",
    Species == "ERISUC" ~ "Lake Chubsucker", 
    Species == "FUNCHR" ~ "Golden Topminnow", 
    Species == "GAMHOL" ~ "Eastern Mosquitofish",
    Species == "HETFOR" ~ "Least Killifish", 
    Species == "LEPMAC" ~ "Bluegill", 
    Species == "LEPMIC" ~ "Redear Sunfish", 
    Species == "LEPPUN" ~ "Spotted Sunfish", 
    Species == "PALPAL" ~ "Grass Shrimp", 
    Species == "POELAT" ~ "Sailfin Molly",
    Species == "PROFAL" ~ "Slough Crayfish"
  )) %>% as.data.frame()

#trophic position per species per habitat-season-slough
windowsFonts(Times = windowsFont("Times New Roman"))

(pre.post.tp.plot <- ggplot(data = pre.post.tp.alpha.plot %>% 
                              mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(alpha_50))), 
                            aes(x = Species, y = TP_50, color = `Invasion Status`, fill = `Invasion Status`))+
    geom_errorbar(aes(ymin = TP_2.5, ymax = TP_97.5), size = 0.5, position = position_dodge(0.3), width = 0.75)+
    geom_point(size = 1, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    scale_y_continuous(limits = c(1.5, 6.2), expand = c(0,0))+
    scale_x_discrete(limits = c("Grass Shrimp", "Eastern Mosquitofish", "Least Killifish", 
                                "Golden Topminnow", "Sailfin Molly", "Dragonfly Larvae", 
                                "Giant Water Bug", "Spotted Sunfish", "Mayan Cichlid",
                                "Slough Crayfish", "Bluegill", "Redear Sunfish", 
                                "Yellow Bullhead Catfish", "Lake Chubsucker"
                                ))+
    ylab("Trophic Position")+
    xlab(NULL)+
    annotate("text", x = 1, y = 4.75, label = "*", size = 3)+
    annotate("text", x = 2, y = 4.75, label = "*", size = 3)+
    annotate("text", x = 3, y = 4.75, label = "*", size = 3)+
    annotate("text", x = 4, y = 4.75, label = "*", size = 3)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.5), panel.grid.major = element_line(size = 0.5),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_blank(),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_blank(),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_blank(), legend.text = element_text(size = 4))
  
)

# ggsave(filename = "pre_post_trophic_position.tiff", 
#        plot = pre.post.tp.plot, 
#        width = 174, height = 60, units = "mm", dpi = 600)


#make the same plot but for alpha instead of trophic position
(pre.post.alpha.plot <- ggplot(data = pre.post.tp.alpha.plot %>% 
                                 mutate(`Invasion Status` = fct_reorder(`Invasion Status`, desc(`Invasion Status`))), 
                               aes(x = Species, y = alpha_50, color = `Invasion Status`, fill = `Invasion Status`))+
    geom_errorbar(aes(ymin = alpha_2.5, ymax = alpha_97.5), size = 0.5, position = position_dodge(0.3), width = 0.75)+
    geom_point(size = 1, position = position_dodge(0.3), shape = 21)+
    scale_color_viridis_d(end = 0.75) +
    scale_fill_viridis_d(end = 0.75) +
    #scale_y_continuous(expand = c(0,0))+
    scale_x_discrete(limits = c("Grass Shrimp", "Eastern Mosquitofish", "Least Killifish", 
                                "Golden Topminnow", "Sailfin Molly", "Dragonfly Larvae", 
                                "Giant Water Bug", "Spotted Sunfish", "Mayan Cichlid",
                                "Slough Crayfish", "Bluegill", "Redear Sunfish", 
                                "Yellow Bullhead Catfish", "Lake Chubsucker"
    ))+
    ylab("Alpha (Ranges from 0, 100% green algae, to 1, 100% detritus)")+
    xlab(NULL)+
    annotate("text", x = 2, y = 1.025, label = "*", size = 3)+
    annotate("text", x = 3, y = 1.025, label = "*", size = 3)+
    annotate("text", x = 8, y = 1.025, label = "*", size = 3)+
    annotate("text", x = 12, y = 1.025, label = "*", size = 3)+
    annotate("text", x = 13, y = 1.025, label = "*", size = 3)+
    annotate("text", x = 14, y = 1.025, label = "*", size = 3)+
    theme_bw()+
    theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 45, vjust = 1.1, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.5), panel.grid.major = element_line(size = 0.5),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 4),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_blank(), legend.text = element_text(size = 4))
  
)

# ggsave(filename = "pre_post_alpha.tiff", 
#        plot = pre.post.alpha.plot, 
#        width = 174, height = 60, units = "mm", dpi = 600)

#combine into a single plot with shared legend
(pre.post.tp.alpha.ggarrange <- ggarrange(pre.post.tp.plot, pre.post.alpha.plot, nrow = 2, ncol = 1, font.label = c(size = 4),
                                          labels = "AUTO", legend = "right", common.legend = T))


# ggsave(filename = "pre_post_tp_alpha.tiff", 
#        plot = pre.post.tp.alpha.ggarrange, 
#        width = 84, height = 126, units = "mm", dpi = 600)
```

## Pre- vs Post-Invasion tables
```{r}
#reformat pairwise tp 
pre.post.direct.tp <- pre.post.pairwiseTP %>% 
  separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>% 
  filter(grepl("Pre", Time)) %>% select(-c(Model)) %>% 
  arrange(Species) %>% 
  unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Status_Species") %>% 
  t() %>% as.data.frame() %>% rownames_to_column(var = "Species") %>% 
  separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>% 
  filter(grepl("Post", Time)) %>% select(-c(Model)) %>% 
  arrange(Species) %>% 
  unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Status_Species") %>% 
  t() %>% as.matrix() 

#reformat tp posterior modes
pre.post.tp.wide <- pre.post.tp.alpha.plot %>% select(`Invasion Status`, Species, contains("TP")) %>% 
  pivot_wider(names_from = `Invasion Status`, values_from = contains("TP")) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  unite("Pre-Invasion 95% C.I.", c(`TP_2.5_Pre-Invasion`, `TP_97.5_Pre-Invasion`), sep = " - ") %>% 
  unite("Post-Invasion 95% C.I.", c(`TP_2.5_Post-Invasion`, `TP_97.5_Post-Invasion`), sep = " - ") %>% 
  rename(`Pre-Invasion Mode` = `TP_50_Pre-Invasion`, `Post-Invasion Mode` = `TP_50_Post-Invasion`) %>% 
  select(Species, contains("Pre"), contains("Post"))
  
#merge pairwise probs and posterior modes for tp
pre.post.tp.table <- pre.post.direct.tp %>% diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.post.direct.tp)) %>% 
  rename(`Pre < Post` = ".") %>% 
  separate(Species, into = c("Time", "Invasion", "Species"), sep = "_") %>% 
  #mutate(Time = gsub("\\[[[0-9][0-9]] ", "", Time)) %>% 
  #unite("Invasion_Status", c(Time, Invasion), sep = "-") %>% 
  select(Species, `Pre < Post`) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "ANISOP" ~ "Anisoptera",
    Species == "BELSPP" ~ "Belostoma spp.",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "FUNCHR" ~ "F. chrysotus", 
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "HETFOR" ~ "H. formosa", 
    Species == "LEPMAC" ~ "L. macrochirus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "PALPAL" ~ "P. paludosus", 
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROFAL" ~ "P. fallax")) %>% 
  merge(., pre.post.tp.wide, by = "Species") %>% 
  mutate(`Percent Change` = ((`Post-Invasion Mode` - `Pre-Invasion Mode`) / `Pre-Invasion Mode`)*100) %>% 
  select(Species, contains("Pre-"), contains("Post-"), everything())

#proportion of comparisons that are statistically different
sum(pre.post.tp.table$`Pre < Post`>=0.95|pre.post.tp.table$`Pre < Post`<=0.05)/nrow(pre.post.tp.table)

#export to csv  
write.csv(pre.post.tp.table, 
          file = "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_tp_table.csv",
          row.names = F)

#now do the same for alpha
#reformat pairwise alpha 
pre.post.direct.alpha <- pre.post.pairwisealpha %>% 
  separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>% 
  filter(grepl("Pre", Time)) %>% select(-c(Model)) %>% 
  arrange(Species) %>% 
  unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Status_Species") %>% 
  t() %>% as.data.frame() %>% rownames_to_column(var = "Species") %>% 
  separate(Species, into = c("Time", "Invasion", "Species", "Model"), sep = "[//.]") %>% 
  filter(grepl("Post", Time)) %>% select(-c(Model)) %>% 
  arrange(Species) %>% 
  unite("Invasion_Status_Species", c(Time, Invasion, Species), sep = "_") %>% 
  column_to_rownames(var = "Invasion_Status_Species") %>% 
  t() %>% as.matrix() 

#reformat alpha posterior modes
pre.post.alpha.wide <- pre.post.tp.alpha.plot %>% select(`Invasion Status`, Species, contains("alpha")) %>% 
  pivot_wider(names_from = `Invasion Status`, values_from = contains("alpha")) %>% 
  mutate(across(where(is.numeric), round, digits = 2)) %>% 
  unite("Pre-Invasion 95% C.I.", c(`alpha_2.5_Pre-Invasion`, `alpha_97.5_Pre-Invasion`), sep = " - ") %>% 
  unite("Post-Invasion 95% C.I.", c(`alpha_2.5_Post-Invasion`, `alpha_97.5_Post-Invasion`), sep = " - ") %>% 
  rename(`Pre-Invasion Mode` = `alpha_50_Pre-Invasion`, `Post-Invasion Mode` = `alpha_50_Post-Invasion`) %>% 
  select(Species, contains("Pre"), contains("Post"))

#merge pairwise probs and posterior modes for alpha
pre.post.alpha.table <- pre.post.direct.alpha %>% diag() %>% as.data.frame() %>% 
  mutate(Species = rownames(pre.post.direct.alpha)) %>% 
  rename(`Pre < Post` = ".") %>% 
  separate(Species, into = c("Time", "Invasion", "Species"), sep = "_") %>% 
  #mutate(Time = gsub("\\[[[0-9][0-9]] ", "", Time)) %>% 
  #unite("Invasion_Status", c(Time, Invasion), sep = "-") %>% 
  select(Species, `Pre < Post`) %>% 
  mutate(Species = case_when(
    Species == "AMENAT" ~ "A. natalis",
    Species == "ANISOP" ~ "Anisoptera",
    Species == "BELSPP" ~ "Belostoma spp.",
    Species == "CICURO" ~ "C. urophthalmus",
    Species == "ERISUC" ~ "E. sucetta", 
    Species == "FUNCHR" ~ "F. chrysotus", 
    Species == "GAMHOL" ~ "G. holbrooki",
    Species == "HETFOR" ~ "H. formosa", 
    Species == "LEPMAC" ~ "L. macrochirus", 
    Species == "LEPMIC" ~ "L. microlophus", 
    Species == "LEPPUN" ~ "L. punctatus", 
    Species == "PALPAL" ~ "P. paludosus", 
    Species == "POELAT" ~ "P. latipinna",
    Species == "PROFAL" ~ "P. fallax")) %>% 
  merge(., pre.post.alpha.wide, by = "Species") %>% 
  mutate(`Percent Change` = ((`Post-Invasion Mode` - `Pre-Invasion Mode`) / `Pre-Invasion Mode`)*100) %>% 
  select(Species, contains("Pre-"), contains("Post-"), everything())

#proportion of comparisons that statistically changed
sum(pre.post.alpha.table$`Pre < Post`>=0.95|pre.post.alpha.table$`Pre < Post`<=0.05)/nrow(pre.post.alpha.table)

write.csv(pre.post.alpha.table, 
           "Outputs/Stable Isotopes/Trophic Position/Pre-vs-Post-Invasion/pre_post_alpha_table.csv",
           row.names = F)
```

# Isotope Biplots
```{r}
#facet_grid post-invasion isotopes by slough, habitat, and season

iso.biplot.data.post.invasion <- all.isotopes %>% 
  filter(!is.na(d13C), !is.na(d15N), !is.na(Habitat), Habitat != "Sawgrass") %>% 
  mutate(size_class = case_when(
    is.na(size_class) ~ .$Species, T ~ size_class),
    Habitat = case_when(
      Habitat %in% c("Mangrove", "Edge") ~ "NP",
      T ~ Habitat)
    ) %>% 
  mutate(across(contains("d1"), as.numeric)) %>% 
  group_by(Species, Slough) %>% 
  summarize(across(c(d13C, d15N), mean))

windowsFonts(Times = windowsFont("Times New Roman"))

(iso.biplot.post.invasion <- ggplot(iso.biplot.data.post.invasion, aes(x = d13C, y = d15N))+
   facet_wrap(~Slough)+
   geom_text(aes(label = Species), size = 0.5)+
   theme_bw()+
   theme(text = element_text(family = "Times", size = 5),
          axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1, size = 2.5),
          strip.text.x = element_text(size = 5),
          strip.text.y = element_text(size = 5),
          #panel.border = element_blank(), panel.grid.major = element_blank(),
          panel.grid.minor = element_line(size = 0.1), panel.grid.major = element_line(size = 0.1),
          axis.line = element_line(color = 'black', size = 0.1), axis.ticks.x = element_line(size = 0.05),
          axis.title = element_text(size = 5), axis.text.x.bottom = element_text(size = 3.5),
          axis.text.y = element_text(size = 4), axis.ticks.y = element_line(size = 0.2),
          legend.title = element_text(size = 5), legend.text = element_text(size = 4))


)


# ggsave(plot = iso.biplot.post.invasion, 
#        filename = "iso_biplot_post_invasion.tiff", 
#        dpi = 600, width = 174, height = 60, units = "mm")

d13c.large <- filter(all.isotopes, d13C > -20) %>% arrange(desc(d13C))

palpal <- pre.post.invasion %>% filter(Species == "PALPAL")
lepmac <- pre.post.invasion %>% filter(Species == "LEPMAC")

pre.post.invasion.mean <- pre.post.invasion %>% 
  filter(d13C < -15.9) %>% 
  group_by(Community, Species) %>% 
  summarize(across(where(is.numeric), list(mean = mean, sd = sd))) %>% 
  arrange(Species) %>% 
  mutate(Name = case_when(
    Species == "AMENAT" ~ "Yellow Bullhead",
    Species == "ANISOP" ~ "Dragonfly Larvae",
    Species == "BELSPP" ~ "Creeping Water Bug",
    Species == "CICURO" ~ "Mayan Cichlid",
    Species == "ERISUC" ~ "Lake Chubsucker",
    Species == "FLOC" ~ "Flocculent Matter",
    Species == "FUNCHR" ~ "Golden Topminnow",
    Species == "GAMHOL" ~ "Eastern Mosquitofish",
    Species == "HETFOR" ~ "Least Killifish",
    Species == "LEPMAC" ~ "Bluegill",
    Species == "LEPMIC" ~ "Redear",
    Species == "LEPPUN" ~ "Spotted Sunfish",
    Species == "PALPAL" ~ "Grass Shrimp",
    Species == "POELAT" ~ "Sailfin Molly",
    Species == "PROFAL" ~ "Slough Crayfish",
    T ~ Species
  ))

pre.post.invasion %>% 
  group_by(Community, Species) %>% 
  tally() %>% 
  arrange(Species) %>% 
  pivot_wider(names_from = Community, values_from = n) %>% 
  write.csv("Outputs/Stable Isotopes/pre_post_invasion_sample_sizes.csv", row.names = F)

(
  iso.biplot.pre.post.invasion <-
    ggplot(data = pre.post.invasion.mean, aes(x = d13C_mean, y = d15N_mean))+
    #facet_wrap(~Community, scales = "free")+
    geom_errorbar(aes(ymin = d15N_mean - d15N_sd, ymax = d15N_mean + d15N_sd), linewidth = 0.25, color = "lightgrey")+
    geom_errorbar(aes(xmin = d13C_mean - d13C_sd, xmax = d13C_mean + d13C_sd), linewidth = 0.25, color = "lightgrey")+
    #geom_text_repel(aes(label = Species, color = Community), size = 1, max.overlaps = 15) +
    geom_line(aes(group = Name), color = "grey")+
    geom_text(aes(label = Name, color = Community), size = 2) +
    #scale_x_continuous(limits = c(-35,-25))+
    scale_color_viridis_d(end = 0.75, breaks = c("Pre-Invasion", "Post-Invasion"), direction = -1)+
    coord_fixed()+
    xlab(expression(paste(delta^{13}, "C (\u2030)")))+
    ylab(expression(paste(delta^{15}, "N (\u2030)")))+
    theme_bw() +
    theme(
      text = element_text(family = "Times", size = 5),
      axis.text.x = element_text(
        #angle = 90,
        #vjust = 0.5,
        #hjust = 1,
        size = 8
      ),
      strip.text.x = element_text(size = 8),
      strip.text.y = element_text(size = 8),
      #panel.border = element_blank(), panel.grid.major = element_blank(),
      panel.grid.minor = element_line(size = 0.1),
      panel.grid.major = element_line(size = 0.1),
      axis.line = element_line(color = 'black', size = 0.1),
      axis.ticks.x = element_line(size = 0.05),
      axis.title = element_text(size = 8),
      axis.text.x.bottom = element_text(size = 8),
      axis.text.y = element_text(size = 8),
      axis.ticks.y = element_line(size = 0.2),
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 8)
    )
)

# ggsave(
#   plot = iso.biplot.pre.post.invasion,
#   filename = "iso_biplot_pre_post_invasion.tiff",
#   dpi = 600, width = 200, height = 150, units = "mm"
# )
```